matlab滤波器外文翻译外文文献英文文献IIR数字滤波器的设计整理
毕业设计(论文)-基于MATLAB的IIR数字滤波器的设计
IIR数字滤波器的设计摘要数字滤波器是对数字信号进行滤波处理以得到期望的响应特性的离散时间系统。
作为一种电子滤波器，数字滤波器与完全工作在模拟信号域的模拟滤波器不同。
数字滤波器工作在数字信号域，它处理的对象是经由采样器件将模拟信号转换而得到的数字信号。
数字滤波器的工作方式与模拟滤波器也完全不同：后者完全依靠电阻、电容、晶体管等电子元件组成的物理网络实现滤波功能；而前者是通过数字运算器件对输入的数字信号进行运算和处理，从而实现设计要求的特性。
本文由数字滤波器的功能、应用及发展入手，介绍了数字滤波器的基本概念，其中包括系统的描述、系统的传递函数和IIR数字滤波器基本结构。
其次根据IIR数字滤波器的设计原理，在MA TLAB环境下分别采用脉冲响应不变法、双线性变换法和MA TLAB函数直接设计法对IIR数字滤波器进行了设计。
最后应用FDATool和Simulink工具对IIR数字滤波器进行了仿真。
关键词：IIR数字滤波器；MATLAB；脉冲响应不变法；双线性变换法；FDATool；SimulinkDesign of IIR digital filterAbstractDigital filters are the discrete-time systems that process to filter digital signal to get expected response characteristics. As an electronic filter, digital filters work differently from the analog signal filters who completely work in analogy signal domain. Digital filter work in the digital signal domain and its targets are digital signals that are received by sampling devices converting analog signals to digital signals. The working methods of digital filters and analog filters are completely different: the latter completely rely on the function of the physical network formed by resistors, capacitors, transistors and other electronic components of filtering ,while the former computes and processes digital signals with the help of digital computing devices to realize the characteristics of the design requirements.In this paper, the function, application and development of the digital filter are introduced followed by the introduction of the principle of digital filter design. The principle first includes the description of the system, the transfer function of the system and the basic structure of the IIR (Infinite Impulse Response) digital filter. Then, according to the design principle of IIR digital filter, the IIR digital filter is designed by the method of non-changing impulse response, the method of double linear transform and direct method using MATLAB functions. At last, the designed IIR digital filter is simulated by FDATool and MATLAB Simulink Tool.Key words:IIR digital filter；MATLAB；non-changing impulse response；double linear transformation；FDATool；Simulink目录第一章绪论 (1)1.1数字滤波器技术概述 (1)1.2滤波器及滤波方法的发展历程 (2)1.3滤波器的分类 (3)1.4数字滤波器的优越性 (4)1.5数字滤波器的实现方法 (5)1.6MATLAB软件简介 (6)1.7MATLAB的语言特点 (8)第二章数字滤波器基础 (10)2.1数字滤波器的基本概念 (10)2.2系统的描述 (11)2.3系统的传递函数 (12)2.4IIR数字滤波器的基本结构 (12)2.4.1直接Ⅰ型 (13)2.4.2直接Ⅱ型 (14)2.4.3级联型 (14)2.4.4并联型 (16)第三章IIR数字滤波器的设计方法及过程 (17)3.1基于脉冲响应不变法的IIR滤波器设计 (17)3.2基于双线性Z变换法的IIR滤波器设计 (20)3.3基于MATLAB函数直接设计IIR数字滤波器 (24)3.3.1巴特沃斯数字滤波器设计 (24)3.3.2切比雪夫Ⅰ型IIR数字滤波器设计 (27)3.3.3切比雪夫Ⅱ型IIR数字滤波器设计 (29)3.3.4基于椭圆法直接设计IIR数字滤波器 (30)3.4FDAT OOL设计法 (33)3.5S IMULINK建模设计法 (37)第四章结论 (41)参考文献 (42)致谢 (43)第一章绪论1.1 数字滤波器技术概述数字滤波器实际上就是一种数字信号处理系统的算法或设备，也可以说是一种运算过程。
IIR数字滤波器的设计外文文献以与翻译
IIRDigitaFilterDesignAn important step in the development of a digital filter is the determination of a realizable transfer function G(z) approximating the given frequency response specifications. If an IIR filter is desired,it is also necessary to ensure that G(z) is stable. The process of deriving the transfer function G(z) is called digital filter design. After G(z) has been obtained, the next step is to realize it in the form of a suitable filter structure. In chapter 8,we outlined a variety of basic structures for the realization of FIR and IIR transfer functions. In this chapter,we consider the IIR digital filter design problem. The design of FIR digital filters is treated in chapter 10.First we review some of the issues associated with the filter design problem. A widely used approach to IIR filter design based on the conversion of a prototype analog transfer function to a digital transfer function is discussed next. Typical design examples are included to illustrate this approach. We then consider the transformation of one type of IIR filter transfer function into another type, which is achieved by replacing the complex variable z by a function of z. Four commonly used transformations are summarized. Finally we consider the computer-aided design of IIR digital filter. To this end, we restrict our discussion to the use of matlab in determining the transfer functions.9.1 preliminary considerationsThere are two major issues that need to be answered before one can develop the digital transfer function G(z). The first and foremost issue is the development of a reasonable filter frequency response specification from the requirements of the overall system in which the digital filter is to be employed. The second issue is to determine whether an FIR or IIR digital filter is to be designed. In the section ,we examine these two issues first .Next we review the basic analytical approach to the design of IIR digital filters and then consider the determination of the filter order that meets the prescribed specifications. We also discuss appropriate scaling of the transfer function.9.1.1 Digital Filter SpecificationsAs in the case of the analog filter,either the magnitude and/or the phase(delay) response is specified for the design of a digital filter for most applications. In some situations, the unit sample response or step response may be specified. In most practical applications, the problem of interest is the development of a realizable approximation to a given magnitude response specification. As indicated in section 4.6.3, the phase response of the designed filter can be corrected by cascading it with an allpass section. The design of allpass phase equalizers has received a fair amount of attention in the last few years. We restrict our attention in this chapter to the magnitude approximation problem only. We pointed out in section 4.4.1 that there are four basic types of filters,whose magnitude responses are shown in Figure 4.10. Since the impulse response corresponding to each of these is noncausal and of infinite length, these ideal filters are not realizable. One way of developing a realizable approximation to these filter would be to truncate the impulse response as indicated in Eq.(4.72) for a lowpass filter. The magnitude response of the FIR lowpass filter obtained by truncating the impulse response of the ideal lowpass filter does not have a sharp transition from passband to stopband but, rather, exhibits a gradual "roll-off."Thus, as in the case of the analog filter design problem outlined in section 5.4.1, the magnitude response specifications of a digital filter in the passband and in the stopband are given with some acceptable tolerances. In addition, a transition band is specified between the passband and the stopband to permit the magnitude to drop off smoothly. For example, the magnitude )(j e G of a lowpass filter may be given as shown in Figure7.1. As indicated in the figure, in the passband defined by 0p ωω≤≤, we require that the magnitude approximates unity with an error of p δ±,i.e.,p p j p for e G ωωδδω≤+≤≤-,1)(1.In the stopband, defined by πωω≤≤s ,we require that the magnitude approximates zero with an error of i s ,δ.e.,,)(s j e G δω≤ forπωω≤≤s . The frequencies p ω and s ω are , respectively, called the passband edge frequency and the stopband edge frequency. The limits of the tolerances in the passband and stopband, p δ and s δ, are usually called the peak ripple values. Note that the frequency response )(ωj e G of a digital filter is a periodic function of ω,and the magnitude response of a real-coefficient digital filter is an even function ofω. As a result, the digital filter specifications are given only for the range πω≤≤0.Digital filter specifications are often given in terms of the loss function,)(log 20)(10ωωζj e G -=, in dB. Here the peak passband ripplep α and the minimum stopband attenuations α are given in dB,i.e., the loss specifications of a digitalfilter are given bydB p p )1(log 2010δα--=,dB s s )(log 2010δα-=.9.1 Preliminary ConsiderationsAs in the case of an analog lowpass filter, the specifications for a digital lowpass filter may alternatively be given in terms of its magnitude response, as in Figure 7.2. Here the maximum value of the magnitude in the passband is assumed to be unity, and themaximum passband deviation, denoted as 1/21ε+,is given by the minimum value of the magnitude in the passband. The maximum stopband magnitude is denoted by 1/A.For the normalized specification, the maximum value of the gain function or the minimum value of the loss function is therefore 0 dB. The quantity max α given bydB )1(log 20210max εα+=Is called the maximum passband attenuation. Forp δ<<1, as is typically the case, itcan be shown thatp p αδα2)21(log 2010max ≅--≅ The passband and stopband edge frequencies, in most applications, are specified in Hz, along with the sampling rate of the digital filter. Since all filter design techniques are developed in terms of normalized angular frequencies p ω and s ω,the sepcified critical frequencies need to be normalized before a specific filter design algorithm can be applied. Let T F denote the sampling frequency in Hz, and F P and F s denote, respectively,the passband and stopband edge frequencies in Hz. Then the normalized angular edge frequencies in radians are given byT F F F F p TpT p p ππω22==Ω= T F F F F s T s T s s ππω22==Ω= 9.1.2 Selection of the Filter TypeThe second issue of interest is the selection of the digital filter type,i.e.,whether an IIR or an FIR digital filter is to be employed. The objective of digital filter design is to develop a causal transfer function H(z) meeting the frequency response specifications. For IIR digital filter design, the IIR transfer function is a real rational function of 1-z . H(z)=N MdNzz d z d d pMz z p z p p ------++++++++ (2211022110)Moreover, H(z) must be a stable transfer function, and for reduced computational complexity, it must be of lowest order N. On the other hand, for FIR filter design, the FIR transfer function is a polynomial in 1-z:∑=-=Nnnz nhzH] [)(For reduced computational complexity, the degree N of H(z) must be as small as possible.In addition, if a linear phase is desired, then the FIR filter coefficients must satisfy the constraint:][][Nnhnh-±=T here are several advantages in using an FIR filter, since it can be designed with exact linear phase and the filter structure is always stable with quantized filter coefficients. However, in most cases, the order N FIR of an FIR filter is considerably higher than the order N IIR of an equivalent IIR filter meeting the same magnitude specifications. In general, the implementation of the FIR filter requires approximately N FIR multiplications per output sample, whereas the IIR filter requires 2N IIR+1 multiplications per output sample. In the former case, if the FIR filter is designed with a linear phase, then the number of multiplications per output sample reduces to approximately (N FIR+1)/2. Likewise, most IIR filter designs result in transfer functions with zeros on the unit circle,and the cascade realization of an IIR filter of orderIIRN with all of the zeros on the unitcircle requires [(3IIRN+3)/2] multiplications per output sample. It has been shown that for most practical filter specifications, the ratio N FIR/N IIR is typically of the order of tens or more and, as a result, the IIR filter usually is computationally more efficient[Rab75]. However ,if the group delay of the IIR filter is equalized by cascading it with an allpass equalizer, then the savings in computation may no longer be that significant [Rab75]. In many applications, the linearity of the phase response of the digital filter is not an issue,making the IIR filter preferable because of the lower computational requirements.9.1.3 Basic Approaches to Digital Filter DesignIn the case of IIR filter design, the most common practice is to convert the digital filter specifications into analog lowpass prototype filter specifications, and then to transform it into the desired digital filter transfer function G(z). This approach has been widely used for many reasons:(a) Analog approximation techniques are highly advanced.(b) They usually yield closed-form solutions.(c) Extensive tables are available for analog filter design.(d) Many applications require the digital simulation of analog filters.In the sequel, we denote an analog transfer function as)()()(s D s P s H a a a =, Where the subscript "a" specifically indicates the analog domain. The digital transfer function derived form H a (s) is denoted by)()()(z D z P z G = The basic idea behind the conversion of an analog prototype transfer function H a (s) into a digital IIR transfer function G(z) is to apply a mapping from the s-domain to the z-domain so that the essential properties of the analog frequency response are preserved. The implies that the mapping function should be such that(a) The imaginary(j Ω) axis in the s-plane be mapped onto the circle of the z-plane.(b) A stable analog transfer function be transformed into a stable digital transfer function.To this end,the most widely used transformation is the bilinear transformation described in Section 9.2.Unlike IIR digital filter design,the FIR filter design does not have any connection with the design of analog filters. The design of FIR filter design does not have anyconnection with the design of analog filters. The design of FIR filters is therefore based on a direct approximation of the specified magnitude response,with the often added requirement that the phase response be linear. As pointed out in Eq.(7.10), a causal FIR transfer function H(z) of length N+1 is a polynomial in z -1 of degree N. The corresponding frequency response is given by∑=-=N n n j j en h e H 0][)(ωω.It has been shown in Section 3.2.1 that any finite duration sequence x[n] of length N+1 is completely characterized by N+1 samples of its discrete-time Fourier transfer X(ωj e ). As a result, the design of an FIR filter of length N+1 may be accomplished by finding either the impulse response sequence {h[n]} or N+1 samples of its frequency response )H(e j ω. Also, to ensure a linear-phase design, the condition of Eq.(7.11) must be satisfied. Two direct approaches to the design of FIR filters are the windowed Fourier series approach and the frequency sampling approach. We describe the former approach in Section 7.6. The second approach is treated in Problem 7.6. In Section 7.7 we outline computer-based digital filter design methods.作者：Sanjit K.Mitra国籍：USA出处：Digital Signal Processing -A Computer-Based Approach 3eIIR数字滤波器的设计在一个数字滤波器发展的重要步骤是可实现的传递函数G（z）的接近给定的频率响应规格。
基于Matlab的IIR数字滤波器设计(论文)
摘要在现代通信系统中，由于信号中经常混有各种复杂成分，所以很多信号分析都是基于滤波器而进行的，而数字滤波器是通过数值运算实现滤波，具有处理精度高、稳定、灵活、不存在阻抗匹配问题，可以实现模拟滤波器无法实现的特殊滤波功能。
数字滤波器根据其冲激响应函数的时域特性，可分为两种，即无限长冲激响应(IIR)数字滤波器和有限长冲激响应(FIR)数字滤波器。
实现IIR滤波器的阶次较低，所用的存储单元较少，效率高，精度高，而且能够保留一些模拟滤波器的优良特性，因此应用很广。
Matlab软件以矩阵运算为基础，把计算、可视化及程序设计有机融合到交互式工作环境中，并且为数字滤波的研究和应用提供了一个直观、高效、便捷的利器。
尤其是Matlab中的信号处理工具箱使各个领域的研究人员可以直观方便地进行科学研究与工程应用。
本文首先介绍了数字滤波器的概念，分类以及设计要求。
接着利用MATLAB函数语言编程，用信号处理图形界面FDATool来设计滤波器以及Sptool界面设计的方法，并用FDATool模拟IIR 数字滤波器处理信号。
重点设计Chebyshev I型和Chebyshev II型数字低通滤波器,并介绍最优化设计。
【关键字】IIR 滤波器FDATool Sptool SimulinkABSTRACTIn modern communication systems,Because often mixed with various signal complex components,So many signal analysis is based on filters, and the digital filter is realized through numerical computation, digital filters filter with high precision, stability and flexibility, don't exist, can realize the impedance matching simulating the special filter cannot achieve filter function. Digital filter according to its impulse response function and characteristics of the time can be divided into two kinds, namely the infinite impulse response (IIR) digital filter and finite impulse response (FIR digital filters). The order of realizing IIR filter is used, low and high efficiency less storage unit, high precision, and can keep some simulation characteristics of filter, so it is widely used. Matlab software based on matrix computation, the calculation, visualization and program design of organic integration to interactive environment for digital filter, and the research and application of provides an intuitive, efficient and convenient tool. Especially in the Matlab signal processing to all areas of research toolbox personnel can easily for scientific research and engineering application. This paper introduces the concept of digital filter, classification and design requirements. Then using MATLAB language programming, with functions of signal processing FDATool graphical interface design of interface design and Sptool filter, and FDATool analog signal processing IIR digital filter. Key design Chebyshev type I and II digital Chebyshev lowpass filter, and introduces optimization design.【Keywords】IIR Filter FDATool Sptool Simulink目录前言 ............................................................. １第一章数字滤波器 ................................................. ２第一节数字滤波器的概念........................................ ２第二节数字滤波器的分类........................................ ２第三节数字滤波器的设计要求.................................... ４第二章 IIR数字滤波器设计方法...................................... ５第一节 IIR数字滤波器的设计步骤................................. ５第二节用脉冲相应不变法设计IIR数字滤波器...................... ６一、设计原理................................................ ６二、脉冲响应不变法优缺点.................................... ８第三节双线性变换法设计IIR数字滤波器.......................... ９一、设计原理................................................ ９二、双线性变换法优缺点.................................... １１第三章 IIR滤波器的MATLAB设计................................... １３第一节 IIR数字滤波器的典型设计法............................. １４第二节 IIR数字滤波器的直接设计法............................. １８第三节 FDATool介绍和界面设计................................. ２３第四节 FDATOOL设计IIR数字滤波器............................. ２４第五节 SIMULINK 仿真IIR滤波器............................... ２６总结 ........................................................... ２９致谢 ........................................................... ３０参考文献 ........................................................ ３１结束语 .......................................................... ３２前言随着信息时代和数字世界的到来，数字信号处理已成为当今一门极其重要的学科和技术领域。
数字信号处理Matlab实验三-IIR数字滤波器的设计
数字信号处理 Matlab 实验三-IIR 数字滤波器的设计1. 概述数字滤波器是数字信号处理领域中的重要内容。
按照系统的特点，数字滤波器可以分为 FIR 数字滤波器和 IIR 数字滤波器。
其中，IIR 数字滤波器具有更强的适应性和更高的性能，因此受到广泛关注。
本文档将详细介绍 Matlab 实验中的 IIR 数字滤波器的设计过程。
2. IIR 数字滤波器的基本概念IIR 数字滤波器是一种反馈型滤波器，它的输出信号取决于当前的输入信号和前一时刻的输出信号。
在 IIR 数字滤波器中，反馈路径与前向路径都包含有延时器和系数。
IIR 数字滤波器的具体实现形式有直接型、级联型、积分型等。
IIR 数字滤波器的主要特征是具有无限脉冲响应。
这一特性意味着输入信号可以产生无限长的输出响应，并且IIR 数字滤波器具有更加平滑的频率响应和更高的滤波器阶数。
3. IIR 数字滤波器设计的步骤Matlab 的 Signal Processing Toolbox 中提供了多种方法进行 IIR 数字滤波器设计。
在本文档中，我们将介绍基于极点和零点设计的方法。
IIR 滤波器设计主要分为以下几个步骤：3.1 确定滤波器类型和性能规格设计 IIR 数字滤波器时，需要先确定滤波器的类型和性能规格。
比如，需要确定滤波器的通带和阻带边界频率、通带和阻带幅度响应、滤波器阶数等参数。
3.2 根据性能规格确定滤波器的传递函数根据滤波器的类型、性能规格、滤波器的传递函数和滤波器结构之间的关系，通过理论计算得到滤波器的传递函数。
3.3 将滤波器传递函数化简为数字滤波器结构将传递函数简化为数字滤波器的结构，选择适当的滤波器结构和方案。
3.4 计算数字滤波器的系数选择一种计算数字滤波器系数的方法，如双线性变换、频率抽取等。
3.5 检验滤波器设计的性能进行模拟仿真和实验检验，根据预设的性能规格检验滤波器设计的合理性。
4. Matlab 实现 IIR 数字滤波器的设计在 Matlab 中，可以使用 Signal Processing Toolbox 中的 iirfilter 函数实现 IIR 数字滤波器的设计。
基于matlab的IIR数字滤波器的设计毕业设计(论文)
基于matlab的IIR数字滤波器的设计摘要：IIR数字滤波器在MATLAB环境下的设计方法和实现方法，在无限脉冲响应（IIR）数字滤波器设计中，先进行模拟滤波器的设计，然后进行模拟—数字滤波器转换，即采用脉冲响应不变法及双线性Z变化法设计数字滤波器，最后进行滤波器的频带转换。
关键词：IIR数字滤波器；matlab；频带转换；引言数字滤波器是数字信号处理的重要基础，数字信号处理主要是研究数字或符号的序列表示信号波形，并用数字的方式去处理这些序列，把它们改变成在某分量和中意义上更希望的形式，以便估计信号的特征参量，或削弱信号中的多余分量和增强信号中的有用分量。
数字滤波器在对信号的过滤、检测与参数估计等处理过程中，是使用最为广泛的一种线性系统。
滤波器的种类很多，从功能上可以分为低通、高通、带通和带阻滤波器，上述每种滤波器又可以分为模拟滤波器和数字滤波器。
如果滤波器的输入输出都是数字信号，则这样的滤波器称之为数字滤波器，它通常通过一定的运算关系改变输入信号所含频率成分的相对比例或者滤除某些频率成分来实现滤波。
根据数字滤波器冲激响应的时域特性，可将数字滤波器分为两种，即无限长冲激响应（IIR）滤波器和有限长冲激响应（FIR）滤波器。
有数字信号处理的一般理论可知，IIR 滤波器的特征是具有无限持续时间的冲激响应，而FIR滤波器使冲激响应只能持续一定的时间。
随着信息时代的到来，数字信号处理已经成为当今一门极其重要的学科和技术，并且在通信、语音、图像、自动控制等众多领域得到了广泛的应用。
在数字信号处理中，数字滤波器占有极其重要的地位，它具有精度高、可靠性好、灵活性大等特点。
现代数字滤波器可以用软件或硬件两种方式来实现。
软件方式实现的优点是可以通过滤波器参数的改变去调整滤波器的性能。
MATLAB是一种面向科学和工程计算的语言，它集数值分析、矩阵运算、信号处理和图形显示于一体，具有编程效率高、调试手段丰富、扩充能力强等特点。
digital-filter-design数字滤波器设计大学毕业论文英文文献翻译及原文
毕业设计（论文）外文文献翻译文献、资料中文题目：数字滤波器设计文献、资料英文题目：digital filter design文献、资料来源：文献、资料发表（出版）日期：院（部）：专业：班级：姓名：学号：指导教师：翻译日期： 2017.02.14毕业设计（论文）外文文献翻译院系：电子与电气工程系年级专业：姓名：学号：附件：digital filter design外文文献：digital filter designAbstract：With the information age and the advent of the digital world, digital signal processing has become one of today's most important disciplines and door technology.Digital signal processing in communications, voice, images, automatic control, radar, military, aerospace, medical and household appliances, and many other fields widely applied. In the digital signal processing applications, the digital filter is important and has been widely applied.Keyword：SCM; Proteus, C language; Digital filter1、figures Unit on :Analog and digital filtersIn signal processing, the function of a filter is to remove unwanted parts of the signal, such as random noise, or to extract useful parts of the signal, such as the components lying within a certain frequency range.The following block diagram illustrates the basic idea.There are two main kinds of filter, analog and digital. They are quite different in their physical makeup and in how they work. An analog filter uses analog electronic circuits made up from components such as resistors, capacitors and op amps to produce the required filtering effect. Such filter circuits are widely used in such applications as noise reduction, video signal enhancement, graphic equalisers in hi-fi systems, and many other areas. There are well-established standard techniques for designing an analog filter circuit for a given requirement. At all stages, the signal being filtered is an electrical voltage or current which is the direct analogue of the physical quantity (e.g. a sound or video signal or transducer output) involved. A digital filter uses a digital processor to perform numerical calculations on sampled values of the signal. The processor may be a general-purpose computer such as a PC, or a specialised DSP (Digital Signal Processor) chip. The analog input signal must first be sampled and digitised using an ADC (analog to digital converter). The resulting binary numbers, representing successive sampled values of the input signal, are transferred to the processor, which carries out numerical calculations on them. These calculations typically involve multiplying the input values by constants and adding the products together. If necessary, the results of these calculations, which now represent sampled values of the filtered signal, are output through a DAC (digital to analog converter) to convert the signal back to analog form.Note that in a digital filter, the signal is represented by a sequence of numbers, rather than a voltage or current.The following diagram shows the basic setup of such a system.Unit refers to the input signals used to filter hardware or software. If the filter input, output signals are separated, they are bound to respond to the impact of the Unit is separated, such as digital filters filter definition. Digital filter function, which was to import sequences X transformation into export operations through a series Y.According to figures filter function 24-hour live response characteristics, digital filters can be divided into two, namely, unlimited long live long live the corresponding IIR filter and the limited response to FIR filters. IIR filters have the advantage of the digital filter design can use simulation results, and simulation filter design of a large number of tables may facilitate simple. It is the shortcomings of the nonlinear phase; Linear phase if required, will use the entire network phase-correction. Image processing and transmission of data collection is required with linear phase filters identity. And FIR linear phase digital filter to achieve, but an arbitrary margin characteristics. Impact from the digital filter response of the units can be divided into two broad categories : the impact of the limited response (FIR) filters, and unlimited number of shocks to (IIR) digital filters.FIR filters can be strictly linear phase, but because the system FIR filter function extremity fixed at the original point, it can only use the higher number of bands to achieve their high selectivity for the same filter design indicators FIR filter called band than a few high-IIR 5-10 times, the cost is higher, Signal delay is also larger. But if the same linear phase, IIR filters must be network-wide calibration phase, the same section also increase the number of filters and network complexity. FIR filters can be used to achieve non-Digui way, not in a limited precision of a shock, and into the homes and quantitative factors of uncertainty arising from the impact of errors than IIR filter small number, and FIR filter can be used FFT algorithms, the computational speed. But unlike IIR filter can filter through the simulation results, there is no ready-made formula FIR filter must use computer-aided design software (such as MATLAB) to calculate. So, a broader application of FIR filters, and IIR filters are not very strict requirements on occasions.Unit from sub-functions can be divided into the following four categories :(1)Low-filter (LPF);(2)high-filter (HPF);(3)belt-filter (BPF);(4)to prevent filter (BSF).The following chart dotted line for the ideals of the filter frequency characteristics :2、MATLAB introducedMATLAB is a matrix laboratory (Matrix Laboratory) is intended. In addition to an excellent value calculation capability, it also provides professional symbols terms, word processing, visualization modeling, simulation and real-time control functions. MATLAB as the world's top mathematical software applications, with a strong engineering computing, algorithms research, engineering drawings, applications development, data analysis and dynamic simulation, and other functions, in aerospace, mechanical manufacturing and construction fields playing an increasingly important role. And the C language function rich, the use of flexibility, high-efficiency goals procedures. High language both advantages as well as low level language features. Therefore, C language is the most widely used programming language. Although MATLAB is a complete, fully functional programming environment, but in some cases, data and procedures with the external environment of the world is very necessary and useful. Filter design using Matlab, could be adjusted with the design requirements and filter characteristics of the parameters, visual simple, greatly reducing the workload for the filter design optimization.In the electricity system protection and secondary computer control, many signal processing and analysis are based on are certain types Yeroskipou and the second harmonics of the system voltage and current signals (especially at D process), are mixed with a variety of complex components, the filter has been installed power system during the critical components. Current computer protection and the introduction of two digitalsignal processing software main filter. Digital filter design using traditional cumbersome formula, the need to change the parameters after recalculation, especially in high filters, filter design workload. Uses MATLAB signal processing boxes can achieve rapid and effective digital filter design and simulatioMATLAB is the basic unit of data matrix, with its directives Biaodashi mathematics, engineering, commonly used form is very similar, it is used to solve a problem than in MATLAB C, Fortran and other languages End precision much the same thing. The popular MATLAB 5.3/Simulink3.0 including hundreds of internal function with the main pack and 30 types of tool kits (Toolbox). kits can be divided into functional tool kits and disciplines toolkit. MA TLAB tool kit used to expand the functional symbols terms, visualization simulation modelling, word processing and real-time control functions. professional disciplines toolkit is a stronger tool kits, tool kits control, signal processing tool kit, tool kits, etc. belonging to such communicationsMATLAB users to open widely welcomed. In addition to the internal function, all the packages MATLAB tool kits are readable document and the document could be amended, modified or users through Yuanchengxu the construction of new procedures to prepare themselves for kits.3、Digital filter designDigital filter design of the basic requirementsDigital filter design must go through three steps :(1) Identification of indicators : In the design of a filter, there must be some indicators. These indicators should be determined on the basis of the application. In many practical applications, digital filters are often used to achieve the frequency operation. Therefore, indicators in the form of general jurisdiction given frequency range and phase response. Margins key indicators given in two ways. The first is absolute indicators. It provides a function to respond to the demands of the general application of FIR filter design. The second indicator is the relative indicators. Its value in the form of answers to decibels. In engineering practice, the most popular of such indicators. For phase response indicators forms, usually in the hope that the system with a linear phase frequency bands human. Using linear phase filter design with the following response to the indicators strengths：①it only contains a few algorithms, no plural operations；②there is delay distortion, onlya fixed amount of delay; ③the filter length N (number of bands for N-1), the volume calculation for N/2 magnitude.(2) Model approach : Once identified indicators can use a previous study of the basic principles and relationships, a filter model to be closer to the target system.(3) Achieved : the results of the above two filters, usually by differential equations, system function or pulse response to describe. According to this description of hardware or software used to achieve it.4、Introduced FPGAProgrammable logic device is a generic logic can use a variety of chips, which is to achieve ASIC ASIC (Application Specific Integrated Circuit) semi-customized device, Its emergence and development of electronic systems designers use CAD tools to designtheir own laboratory in the ASIC device. Especially FPGA (Field Programmable Gate Array) generated and development, as a microprocessor, memory, the figures for electronic system design and set a new industry standard (that is based on standard product sales catalogue in the market to buy). Is a digital system for microprocessors, memories, FPGA or three standard building blocks constitute their integration direction.Digital circuit design using FPGA devices, can not only simplify the design process and can reduce the size and cost of the entire system, increasing system reliability. They do not need to spend the traditional sense a lot of time and effort required to create integrated circuits, to avoid the investment risk and become the fastest-growing industries of electronic devices group. Digital circuit design system FPGA devices using the following main advantages(1) Design flexibleUse FPGA devices may not in the standard series device logic functional limitations. And changes in system design and the use of logic in any one stage of the process, and only through the use of re-programming the FPGA device can be completed, the system design provides for great flexibility.(2)Increased functional densityFunctional density in a given space refers to the number of functional integration logic. Programmable logic chip components doors several high, a FPGA can replace several films, film scores or even hundreds of small-scale digital IC chip illustrated in the film. FPGA devices using the chip to use digital systems in small numbers, thus reducing the number of chips used to reduce the number of printed size and printed, and will ultimately lead to a reduction in the overall size of the system.(3)Improve reliabilityPrinting plates and reduce the number of chips, not only can reduce system size, but it greatly enhanced system reliability. A higher degree of integration than systems in many low-standard integration components for the design of the same system, with much higher reliability. FPGA device used to reduce the number of chips required to achieve the system in the number printed on the cord and joints are reduced, the reliability of the system can be mproved.(4)Shortening the design cycleAs FPGA devices and the programmable flexibility, use it to design a system for longer than traditional methods greatly shortened. FPGA device master degrees high, use printed circuit layout wiring simple. At the same time, success in the prototype design, the development of advanced tools, a high degree of automation, their logic is very simple changes quickly. Therefore, the use of FPGA devices can significantly shorten the design cycle system, and speed up the pace of product into the market, improving product competitiveness.(5)Work fastFPGA/CPLD devices work fast, generally can reach several original Hertz, far larger than the DSP device. At the same time, the use of FPGA devices, the system needed to achieve circuit classes and small, and thus the pace of work of the entire system will be improved.(6)Increased system performance confidentialityMany FPGA devices have encryption functions in the system widely used FPGA devices can effectively prevent illegal copying products were others(7)To reduce costsFPGA device used to achieve digital system design, if only device itself into the price, sometimes you would not know it advantages, but there are many factors affecting the cost of the system, taken together, the cost advantages of using FPGA is obvious. First, the use of FPGA devices designed to facilitate change, shorten design cycles, reduce development costs for system development; Secondly, the size and FPGA devices allow automation needs plug-ins, reducing the manufacturing system to lower costs; Again, the use of FPGA devices can enhance system reliability, reduced maintenance workload, thereby lowering the cost of maintenance services for the system. In short, the use of FPGA devices for system design to save costs.FPGA design principles :FPGA design an important guiding principles : the balance and size and speed of exchange, the principles behind the design of the filter expression of a large number of certification.Here, "area" means a design exertion FPGA/CPLD logic resources of the FPGA can be used to the typical consumption (FF) and the search table (IUT) to measure more general measure can be used to design logic equivalence occupied by the door is measured. "pace" means stability operations in the chip design can achieve the highest frequency, the frequency of the time series design situation, and design to meet the clock cycle -- PADto pad, Clock Setup Time, Clock Hold Beijing, Clock-to-Output Delay, and other characteristics of many time series closely related. Area (area) and speed (speed) runs through the two targets FPGA design always is the ultimate design quality evaluation criteria. On the size and speed of the two basic concepts : balance of size and speed and size and speed of swap.One pair of size and speed is the unity of opposites contradictions body. Requirements for the design of a design while the smallest, highest frequency of operation is unrealistic. More scientific goal should be to meet the design requirements of the design time series (includes requirements for the design frequency) premise, the smallest chip area occupied. Or in the specified area, the design time series cushion greater frequency run higher. This fully embodies the goals of both size and speed balanced thinking. On the size and speed requirements should not be simply interpreted as raising the level and design engineers perfect sexual pursuit, and should recognize that they are products and the quality and cost of direct relevance. If time series cushion larger design, running relatively high frequency, that the design Jianzhuangxing stronger, more quality assurance system as a whole; On the other hand, the smaller size of consumption design is meant to achieve in chip unit more functional modules, the chip needs fewer, the entire system has been significantly reduced cost. As a contradiction of the two components, the size and speed is not the same status. In contrast, meet the timetables and work is more important for some frequency when both conflicts, the use of priority guidelines.Area and the exchange rate is an important FPGA design ideas. Theoretically, if a design time series cushion larger, can run much higher than the frequency designrequirements, then we can through the use of functional modules to reduce the consumption of the entire chip design area, which is used for space savings advantages of speed; Conversely, if the design of a time series demanding, less than ordinary methods of design frequency then generally flow through the string and data conversion, parallel reproduction of operational module, designed to take on the whole "string and conversion" and operate in the export module to chip in the data "and string conversion" from the macro point of view the whole chip meets the requirements of processing speed, which is equivalent to the area of reproduction - rate increase.For example. Assuming that the digital signal processing system is 350Mb/s input data flow rate, and in FPGA design, data processing modules for maximum processing speed of150Mb/s, because the data throughput processing module failed to meet requirements, it is impossible to achieve directly in the FPGA. Such circumstances, they should use "area-velocity" thinking, at least three processing modules from the first data sets will be imported and converted, and then use these three modules parallel processing of data distribution, then the results "and string conversion," we have complete data rate requirements. We look at both ends of the processing modules, data rate is 350Mb/s, and in view of the internal FPGA, each sub-module handles the data rate is 150Mb/s, in fact, all the data throughput is dependent on three security modules parallel processing subsidiary completed, that is used by more chip area achieve high-speed processing through "the area of reproduction for processing speed enhancement" and achieved design.FPGA is the English abbreviation Field of Programmable Gate Array for the site programmable gate array, which is in Pal, Gal, Epld, programmable device basis to further develop the product. It is as ASIC (ASIC) in the field of a semi-customized circuit and the emergence of both a customized solution to the shortage circuit, but overcome the original programmable devices doors circuit few limited shortcomings.FPGA logic module array adopted home (Logic Cell Array), a new concept of internal logic modules may include CLB (Configurable Logic Block), export import module IOB (Input Output Block) and internal links (Interconnect) 3. FPGA basic features are :(1)Using FPGA ASIC design ASIC using FPGA circuits, the chip can be used,while users do not need to vote films production.(2)FPGA do other customized or semi-customized ASIC circuits throughout the Chinese specimen films.(3)FPGA internal capability and rich I/O Yinjue.(4)FPGA is the ASIC design cycle, the shortest circuit, the lowest development costs, risks among the smallest device(5)FPGA using high-speed Chmos crafts, low consumption, with CMOS, TTL low-power compatibleIt can be said that the FPGA chip is for small-scale systems to improve system integration, reliability one of the bestCurrently FPGA many varieties, the Revenue software series, TI companies TPC series, the fiex ALTERA company seriesFPGA is stored in films from the internal RAM procedures for the establishment ofthe state of its work, therefore, need to programmed the internal Ram. Depending on the different configuration, users can use a different programming methodsPlus electricity, FPGA, EPROM chips will be read into the film, programming RAM 中data, configuration is completed, FPGA into working order. Diaodian, FPGA resume into white films, the internal logic of relations disappear, FPGA to repeated use. FPGA's programming is dedicated FPGA programming tool, using generic EPROM, prom programming device can. When the need to modify functional FPGA, EPROM can only change is. Thus, with a FPGA, different programming data to produce different circuit functions. Therefore, the use of FPGA very flexible.There are a variety of FPGA model : the main model for a parallel FPGA plus a EPROM manner; From the model can support a number of films FPGA; serial prom programming model could be used serial prom FPGA programming FPGA; The external model can be engineered as microprocessors from its programming microprocessors.Verilog HDL is a hardware description language for the algorithm level, doors at the level of abstract level to switch-level digital system design modelling. Modelling of the target figure by the complexity of the system can be something simple doors and integrity of electronic digital systems. Digital system to the levels described, and in the same manner described in Hin-time series modelling.Verilog HDL language with the following description of capacity : design behaviour characteristics, design data flow characteristics, composition and structure designed to control and contain the transmission and waveform design a certification mechanism. All this with the use of a modelling language. In addition, Verilog HDL language programming language interface provided by the interface in simulation, design certification from the external design of the visit, including specific simulation control and operation.Verilog HDL language grammar is not only a definition, but the definition of each grammar structure are clear simulation, simulation exercises. Therefore, the use of such language to use Verilog simulation models prepared by a certification. From the C programming language, the language inherited multiple operating sites and structures. Verilog HDL provides modelling capacity expansion, many of the initial expansion would be difficult to understand. However, the core subsets of Verilog HDL language very easy to learn and use, which is sufficient for most modelling applications. Of course, the integrity of the hardware description language is the most complex chips from the integrity of the electronic systems described.HistoryVerilog HDL language initially in 1983 by Gateway Design Automation companies for product development simulator hardware modelling language. Then it is only a dedicated language. Since their simulation, simulation devices widely used products, Verilog HDL as a user-friendly and practical language for many designers gradually accepted. In an effort to increase the popularity of the language activities, Verilog HDL language in 1990 was a public area. Open Verilog International (OVI) is to promote the development of Verilog international organizations. 1992, decided to promote OVI OVI standards as IEEE Verilog standards. The effort will ultimately succeed, a IEEE 1995 Verilog language standard, known as IEEE Std 1364-1995. Integrity standardsin Verilog hardware description language reference manual contains a detailed description.Main capacityListed below are the main Verilog hardware description language ability*Basic logic gate, and, for example, or have embedded in the language and nand* Users of the original definition of the term (UDP), the flexibility. Users can be defined in the original language combinations logic original language, the original language of logic could also be time series* Switches class infrastructure models, such as the nmos and pmos also be embedded in the language* Hin-language structure designated for the cost of printing the design and trails Shi Shi and design time series checks.* Available three different ways to design or mixed mode modelling. These methods include : acts described ways - use process of structural modelling; Data flow approach - use of a modelling approach Fuzhi expression; Structured way - using examples of words to describe modular doors and modelling.* Verilog HDL has two types of data : data types and sequence data line network types. Line network types that the physical links between components and sequence types that abstract data storage components.* To describe the level design, the structure can be used to describe any level module example* Design size can be arbitrary; Language is design size (size) impose any restrictions* And the machine can read Verilog language, it may as EDA tools and languages of the world between the designers* Verilog HDL language to describe capacity through the use of programming language interface (PLI) mechanism further expansion. PLI is to allow external functions of the visit Verilog module information, allowing designers and simulator world Licheng assembly* Design to be described at a number of levels, from the switch level, doors level, register transfer level (RTL) to the algorithm level, including the level of process and content* To use embedded switching level of the original language in class switch design integrity modelling * Same language can be used to generate simulated incentive and certification by the designated testing conditions, such as the value of imports of the designated*Verilog HDL simulation to monitor the implementation of certification, the certification process of implementing the simulation can be designed to monitor and demonstrate value. These values can be used to compare with the expectations that are not matched in the case of print news reports.* Acts described in the class, not only in the RTL level Verilog HDL design description, and to describe their level architecture design algorithm level behavioural description* Examples can use doors and modular structure of language in a class structure described* Verilog HDL mixed mode modelling capabilities in the design of a different design in each module can level modelling* Verilog HDL has built-in logic function, such as*Structure of high-level programming languages, such as conditions of expression, and the cycle of expression language, language can be used* To it and can display regular modelling * Provide a powerful document literacy* Language in the specific circumstances of non-certainty that in the simulator, different models can produce different results; For example, describing events in the standard sequence of events is not defined.5、In troduction of DSPToday, DSP is w idely used in the modern techno logy and it has been the key part of many p roducts and p layed more and mo re impo rtant ro le in our daily life.Recent ly, Northw estern Po lytechnica lUniversity Aviation Microelect ronic Center has comp leted the design of digital signal signal p rocesso r co re NDSP25, w h ich is aim ing at TM S320C25 digital signal p rocesso r of Texas Inst rument TM S320 series. By using top 2dow n design flow , NDSP25 is compat ible w ith inst ruct ion and interface t im ing of TM S320C25.Digital signal processors (DSP) is a fit for real-time digital signal processing for high-speed dedicated processors, the main variety used for real-time digital signal processing to achieve rapid algorithms. In today's digital age background, the DSP has become the communications, computer, and consumer electronics products, and other fields based device.Digital signal processors and digital signal processing is inseparably, we usually say "DSP" can also mean the digital signal processing (Digital Signal Processing), is that in this digital signal processors Lane. Digital signal processing is a cover many disciplines applied to many areas and disciplines, refers to the use of computers or specialized processing equipment, the signals in digital form for the collection, conversion, recovery, valuation, enhancement, compression, identification, processing, the signals are compliant form. Digital signal processors for digital signal processing devices, it is accompanied by a digital signal processing to produce. DSP development process is broadly divided into three phases : the 20th century to the 1970s theory that the 1980s and 1990s for the development of products. Before the emergence of the digital signal processing in the DSP can only rely on microprocessors (MPU) to complete. However, the advantage of lower high-speed real-time processing can not meet the requirements. Therefore, until the 1970s, a talent made based DSP theory and algorithms. With LSI technology development in 1982 was the first recipient of the world gave birth to the DSP chip. Years later, the second generation based on CMOS工艺DSP chips have emerged. The late 1980s, the advent of the third generation of DSP chips. DSP is the fastest-growing 1990s, there have been four successive five-generation and the generation DSP devices. After 20 years of development, the application of DSP products has been extended to people's learning, work and all aspects of life and gradually become electronics products determinants.REFERENCES1.Chan, D.S.K., Rabiner L.R.: Analysis of Quantization Errors in the Direct Form for Finite Impulse。
基于MATLAB的IIR数字滤波器的设计及应用
IIR滤波器设计方法有两类，经常用到的一类设计方法是借助于模拟滤波器的设计方法进行的。其设计思路是：先设计模拟滤波器得到传输函数 ，然后将 按某种方法转换为数字滤波器的系统函数 。这一类方法是基于模拟滤波器的设计方法相对比较成熟，它不仅有完整的设计公式，也有完整的图标供查阅，更可以直接调用MATLAB中的对应的函数进行设计。另一种是直接在频域或者时域中进行设计，设计时必须使用计算机辅助，直接调用MATLAB中的程序或函数即可设计。
附录B程序清单33
第
数字滤波是数字信号处理的重要基础，数字信号处理主要是研究用数字或符号的序列来表示信号波形，并用数字的方式去处理这些序列，把它们改变成在某种意义上更希望的形式，以便估计信号的特征参量，或削弱信号中的多余分量和增强信号中的有用分量。数字滤波器在对信号的过滤、检测与参数估计等处理过程中，是使用最为广泛的一种线性系统。
Digital filter uses discrete system characteristics to conduct processing and transformation to the system input signal, change the input sequence spectrum or signal waveform, let the useful frequency components through, inhibit the outputing of unwanted signal components. Industrial parts throughout our daily necessities, but in the processing of industrial parts often appears scratches, abrasions, mark phenomenon, in order to collection qualified industrial parts effectively,using digital filter analysis to noise forms (such as salt and pepper noise) simulating the acquisition of industrial parts of the dust, mark, followed by the de-noising and filtering to obtain the purpose of qualified parts.
基于matlab的IIR滤波器的设计---文献综述
文献综述数字滤波器的研究及IIR滤波器的设计摘要：文章对数字滤波器做了较为全面的介绍。
概括了滤波器的背景知识、应用以及较为详细的分类情况。
比较了几种有代表性的经典的数字滤波器。
简述了IIR滤波器的设计。
然后对数字滤波器以及发展的走势进行了展望。
关键词：数字滤波器；IIR滤波器；FIR滤波器1. 引言随着信息科学与计算技术的迅速发展，数字信号处理的理论与应用得到飞跃式发展，形成了一门极为重要的学科[1 ]。
不仅如此，它还以不同的形式影响及渗透到其他的学科中去。
不论是国民经济或者是国防建设都与之息息相关，紧密相连。
我们现实生活中会遇到多种多样的信号，例如广播信号、电视信号、雷达信号、通信信号、导航信号、射电天文信号、生物医学信号、控制信号、气象信号、地震勘探信号、机械振动信号、遥感遥测信号等等。
上述这些信号大部分是模拟信号，也有小部分是数字信号。
模拟信号是自变量的连续函数，自变量可以是一维的，也可以是二维或多维的。
大多数情况下一维模拟信号的自变量是时间，经过时间上的离散化(采样)和幅度上的离散化(量化)，这类模拟信号便成为一维数字信号。
滤波技术是信号分析、处理技术的重要分支。
无论是信号的获取、传输, 还是信号的处理和交换都离不开滤波技术, 它对信号安全可靠和有效灵活地传递是至关重要的[ 3 ]。
数字信号的滤波是通过数字滤波器来实现的。
数字滤波器是一种用来过滤时间离散信号的数字系统,它是通过对抽样数据进行数学处理来达到频域滤波的目的[4-5 ]。
2. 数字滤波器分类按照不同的分类方法，数字滤波器有许多种类，但是总起来可以分为两大类：经典滤波器和现代滤波器。
而经典滤波器从滤波特性上来分，则可以分为低通、高通、带通和带阻等滤波器。
以上这些理想滤波器是不可能实现的，因为它们的单位脉冲响应均是非因果且无限长的，我们只能按照某些标准设计滤波器，使之接近理想滤波器[6-7 ]。
数字滤波器从实现的网络结构或从单位脉冲响应长度分类，可以分成无限长单位脉冲响应（IIR ）滤波器和有限长单位脉冲响应（FIR ）滤波器。
基于iir数字滤波器的设计matlab大学论文
引言MATLAB是矩阵实验室（Matrix Laboratory）的简称，是美国MathWorks公司出品的商业数学软件，用于算法开发、数据可视化、数据分析以及数值计算的高级技术计算语言和交互式环境，主要包括MATLAB和Simulink两大部分。
MATLAB和Mathematica、Maple并称为三大数学软件。
它在数学类科技应用软件中在数值计算方面首屈一指。
MATLAB可以进行矩阵运算、绘制函数和数据、实现算法、创建用户界面、连接其他编程语言的程序等，主要应用于工程计算、控制设计、信号处理与通讯、图像处理、信号检测、金融建模设计与分析等领域。
MATLAB的基本数据单位是矩阵，它的指令表达式与数学、工程中常用的形式十分相似，故用MATLAB来解算问题要比用C，FORTRAN等语言完成相同的事情简捷得多，并且mathwork也吸收了像Maple等软件的优点,使MATLAB成为一个强大的数学软件。
在新的版本中也加入了对C，FORTRAN，C++ ，JAVA的支持。
可以直接调用,用户也可以将自己编写的实用程序导入到MATLAB函数库中方便自己以后调用，此外许多的MATLAB爱好者都编写了一些经典的程序，用户可以直接进行下载就可以用MATLAB语言是一种面向科学与工程计算的高级语言，它集科学计算，自动控制，信号处理、神经网络和图象处理等于一体，具有极高的编程效率。
它是一个高级的数学分析与运算软件，可用作动态系统的建模与仿真。
I I R数字滤波器的设计正文一、MATLAB语言的简介1、MATLAB的特点及优势MATLAB作为一种使用广泛的数学软件，具有强大的编程能力，可以进行矩阵的运算、绘制函数和数据，实现算法、创建用户界面、连接其他编程语言的程序等。
它具有以下几个显著特点：●此高级语言可用于技术计算●此开发环境可对代码、文件和数据进行管理●交互式工具可以按迭代的方式探查、设计及求解问题●数学函数可用于线性代数、统计、傅立叶分析、筛选、优化以及数值积分等●二维和三维图形函数可用于可视化数据●各种工具可用于构建自定义的图形用户界面●各种函数可将基于MATLAB的算法与外部应用程序和语言（如C、C++、Fortran、Java、COM 以及Microsoft Excel）集成●不支持大写输入，内核仅仅支持小写同时MATLAB和Mathematica、Maple并称为三大数学软件，自然有它的显著优势，以下简单的介绍它的优势。
基于Matlab的IIR数字滤波器设计(论文)
在现代通信系统中，由于信号中经常混有各种复杂成分，所以很多信号分析都是基于滤波器而进行的，而数字滤波器是通过数值运算实现滤波，具有处理精度高、稳定、灵活、不存在阻抗匹配问题，可以实现模拟滤波器无法实现的特殊滤波功能。
数字滤波器根据其冲激响应函数的时域特性，可分为两种，即无限长冲激响应(IIR)数字滤波器和有限长冲激响应(FIR)数字滤波器。
实现IIR滤波器的阶次较低，所用的存储单元较少，效率高，精度高，而且能够保留一些模拟滤波器的优良特性，因此应用很广。
Matlab软件以矩阵运算为基础，把计算、可视化及程序设计有机融合到交互式工作环境中，并且为数字滤波的研究和应用提供了一个直观、高效、便捷的利器。
尤其是Matlab中的信号处理工具箱使各个领域的研究人员可以直观方便地进行科学研究与工程应用。
本文首先介绍了数字滤波器的概念，分类以及设计要求。
接着利用MATLAB函数语言编程，用信号处理图形界面FDATool来设计滤波器以及Sptool界面设计的方法，并用FDATool 模拟IIR 数字滤波器处理信号。
重点设计Chebyshev I型和Chebyshev II型数字低通滤波器,并介绍最优化设计。
关键字】IIR 滤波器FDATool Sptool SimulinkABSTRACTIn modern communication systems, Because often mixed with various signal complex components, So many signal analysis is based on filters, and the digital filter is realized through numerical computation, digital filters filter with high precision, stability and flexibility, don't exist, can realize the impedance matching simulating the special filter cannot achieve filter function. Digital filter according to its impulse response function and characteristics of the time can be divided into two kinds, namely the infinite impulse response (IIR) digital filter and finite impulse response (FIR digital filters). The order of realizing IIR filter is used, low and high efficiency less storage unit, high precision, and can keep some simulation characteristics of filter, so it is widely used. Matlab software based on matrix computation, the calculation, visualization and program design of organic integration to interactive environment for digital filter, and the research and application of provides an intuitive, efficient and convenient tool. Especially in the Matlab signal processing to all areas of research toolbox personnel can easily for scientific research and engineering application. This paper introduces the concept of digital filter, classification and design requirements. Then using MATLAB language programming, with functions of signal processing FDATool graphical interface design of interface design and Sptool filter, and FDATool analog signal processing IIR digital filter. Key design Chebyshev type I and II digital Chebyshev lowpass filter, and introduces optimization design.【Keywords】IIR Filter FDATool Sptool Simulink目录前言............................................................ １第一章数字滤波器................................................ ２第一节数字滤波器的概念 ...................................... ２第二节数字滤波器的分类 ...................................... ２第三节数字滤波器的设计要求 .................................. ４第二章 IIR 数字滤波器设计方法.................................... ５第一节 IIR 数字滤波器的设计步骤 .............................. ５第二节用脉冲相应不变法设计 IIR数字滤波器.................... ６一、设计原理............................................... ６二、脉冲响应不变法优缺点.................................. ８第三节双线性变换法设计 IIR数字滤波器........................ ９一、设计原理............................................... ９二、双线性变换法优缺点.................................. １１第三章 IIR滤波器的 MATLAB设计................................. １３第一节 IIR 数字滤波器的典型设计法 ........................... １４第二节 IIR 数字滤波器的直接设计法 .......................... １８第三节 FDATool 介绍和界面设计 .............................. ２３第四节 FDATOOL 设计IIR 数字滤波器.......................... ２４第五节 SIMULINK 仿真 IIR滤波器............................. ２６总结.......................................................... ２９致谢.......................................................... ３０参考文献....................................................... ３１结束语......................................................... ３２前言随着信息时代和数字世界的到来，数字信号处理已成为当今一门极其重要的学科和技术领域。
数字滤波器中英文对照外文翻译文献
中英文对照翻译基于VB和Matlab的数字滤波器的设计摘要数字信号处理的核心是数字滤波器的设计。
目前，大多数数字滤波器是基于Matlab这种高性能的数值计算并提供强大的图形显示功能的软件。
MATLAB广泛应用于工程计算，数值分析等多个领域，但它不善于开发接口。
在本文中，将用VB与Matlab混合编程的方法引入到设计数字滤波器中。
集成的软件可以利用VB 和Matlab的最大优势，实现过程表明，该方法简单，方便。
关键词:数字滤波器，Visual Basic，MATLAB，组件对象模型。
1.引言如今，滤波器在相关的电子系统中很重要，因为他们存在于几乎所有的电子系统。
例如，通信系统中广泛利用滤波器的将噪声和所需信号区分开来。
电源供应器使用滤波器来滤除纹波和改善直流信号的质量。
音频均衡器使用过滤器来放大或衰减频段的音频范围，音频质量的提高取决于房间的声学特性。
数字视频由于编码和传输，需要将数字滤波器接入噪声信道，以减少噪声，依此类推。
然而，滤波器的设计是一个密集的计算任务，需要一个大量数值计算得到的滤波器传递函数的任一参数或为一个滤波电路实现的元素的值。
另外，在日常生活中，电脑的使用已经很普及。
因此，计算机软件开发已经成为技术发展的一个重要组成部分。
教育很大部分受这个发展的影响。
今天，大量的软件包可用于设计滤波器，Matlab便是其中之一。
Matlab是由Mathworks公司开发，是一款高性能的数值计算软件，并提供图形显示的强大功能，它被广泛应用于工程计算，数值分析等领域。
现在任何一所大学或工业都在使用Matlab，并且在电路和系统的设计等许多其它事情都会用到。
其中，Matlab的主要特点是，它的一套工具箱在滤波器的设计中都可以使用。
不足的是，使用这些工具箱，需要相当长的时间去掌握它们，新手才能使用它们。
更重要的是，Matlab不善于开发接口。
相反，VB中有一个友好的设计用户界面和开发应用程序，但它不能够计算，尤其是在数字滤波器的设计中。
数字滤波器外文翻译
中文5590字毕业设计（外文翻译材料）2009年6月学 院： 专 业： 学生姓名： 指导教师： 电气与电子工程学院 电子信息工程0503DIGITAL FILTERSDigital filtering is one of the most powerful tools of DSP. Apart from the obvious advantages of virtually eliminating errors in the filter associated with passive component fluctuations over time and temperature, op amp drift (active filters), etc., digital filters are capable of performance specifications that would, at best, be extremely difficult, if not impossible, to achieve with an analog implementation. In addition, the characteristics of a digital filter can be easily changed under software control. Therefore, they are widely used in adaptive filtering applications in communications such as echo cancellation in modems, noise cancellation, and speech recognition.The actual procedure for designing digital filters has the same fundamental elements as that for analog filters. First, the desired filter responses are characterized, and the filter parameters are then calculated. Characteristics such as amplitude and phase response are derived in the same way. The key difference between analog and digital filters is that instead of calculating resistor, capacitor, and inductor values for an analog filter, coefficient values are calculated for a digital filter. So for the digital filter, numbers replace the physical resistor and capacitor components of the analog filter. These numbers reside in a memory as filter coefficients and are used with the sampled data values from the ADC to perform the filter calculations.The real-time digital filter, because it is a discrete time function, works with digitized data as opposed to a continuous waveform, and a new data point is acquired each sampling period. Because of this discrete nature, data samples are referenced as numbers such as sample 1, sample 2, sample 3, etc. Figure 1 shows a low frequency signal containing higher frequency noise which must be filtered out. This waveform must be digitized with an ADC to produce samples x(n). These data values are fed to the digital filter, which in this case is a lowpass filter. The output data samples, y(n), are used to reconstruct an analog waveform using a low glitch DAC.Digital filters, however, are not the answer to all signal processing filtering requirements. In order to maintain real-time operation, the DSP processor must be able to execute all the steps in the filter routine within one sampling clock period1/f s.A fast general purpose fixed-point DSP such as the ADSP-2189M at 75MIPS can 。
matlab_滤波器_外文翻译_外文文献_英文文献_IIR数字滤波器的设计
IIR Digital Filter DesignAn important step in the development of a digital filter is the determination of a realizable transfer function G(z) approximating the given frequency response specifications. If an IIR filter is desired,it is also necessary to ensure that G(z) is stable. The process of deriving the transfer function G(z) is called digital filter design. After G(z) has been obtained, the next step is to realize it in the form of a suitable filter structure. In chapter 8,we outlined a variety of basic structures for the realization of FIR and IIR transfer functions. In this chapter,we consider the IIR digital filter design problem. The design of FIR digital filters is treated in chapter 10.First we review some of the issues associated with the filter design problem. A widely used approach to IIR filter design based on the conversion of a prototype analog transfer function to a digital transfer function is discussed next. Typical design examples are included to illustrate this approach. We then consider the transformation of one type of IIR filter transfer function into another type, which is achieved by replacing the complex variable z by a function of z. Four commonly used transformations are summarized. Finally we consider the computer-aided design of IIR digital filter. To this end, we restrict our discussion to the use of matlab in determining the transfer functions.9.1 preliminary considerationsThere are two major issues that need to be answered before one can develop the digital transfer function G(z). The first and foremost issue is the development of a reasonable filter frequency response specification from the requirements of the overall system in which the digital filter is to be employed. The second issue is to determine whether an FIR or IIR digital filter is to be designed. In the section ,we examine these two issues first . Next we review the basic analytical approach to the design of IIR digital filters and then consider the determination of the filter order that meets the prescribed specifications. Wealso discuss appropriate scaling of the transfer function.9.1.1 Digital Filter SpecificationsAs in the case of the analog filter,either the magnitude and/or the phase(delay) response is specified for the design of a digital filter for most applications. In some situations, the unit sample response or step response may be specified. In most practical applications, the problem of interest is the development of a realizable approximation to a given magnitude response specification. As indicated in section 4.6.3, the phase response of the designed filter can be corrected by cascading it with an allpass section. The design of allpass phase equalizers has received a fair amount of attention in the last few years. We restrict our attention in this chapter to the magnitude approximation problem only. We pointed out in section 4.4.1 that there are four basic types of filters,whose magnitude responses are shown in Figure 4.10. Since the impulse response corresponding to each of these is noncausal and of infinite length, these ideal filters are not realizable. One way of developing a realizable approximation to these filter would be to truncate the impulse response as indicated in Eq.(4.72) for a lowpass filter. The magnitude response of the FIR lowpass filter obtained by truncating the impulse response of the ideal lowpass filter does not have a sharp transition from passband to stopband but, rather, exhibits a gradual "roll-off."Thus, as in the case of the analog filter design problem outlined in section 5.4.1, the magnitude response specifications of a digital filter in the passband and in the stopband are given with some acceptable tolerances. In addition, a transition band is specified between the passband and the stopband to permit the magnitude to drop off smoothly. For example, the magnitude )(ωj e G of a lowpass filter may be given as shown in Figure7.1. As indicated in the figure, in the passband defined by 0p ωω≤≤, we require that themagnitude approximates unity with an error of p δ±,i.e.,p p j p f o r e G ωωδδω≤+≤≤-,1)(1.In the stopband, defined by πωω≤≤s ,we require that the magnitude approximateszero with an error of i s ,δ.e.,,)(s j e G δω≤ for πωω≤≤s .The frequencies p ω and s ω are , respectively, called the passband edge frequency andthe stopband edge frequency. The limits of the tolerances in the passband and stopband, p δ and s δ, are usually called the peak ripple values. Note that the frequency response)(ωj e G of a digital filter is a periodic function of ω,and the magnitude response of a real-coefficient digital filter is an even function of ω. As a result, the digital filter specifications are given only for the range πω≤≤0.Digital filter specifications are often given in terms of the loss function,)(log 20)(10ωωζj e G -=, in dB. Here the peak passband ripple p α and theminimum stopband attenuation s α are given in dB,i.e., the loss specifications of a digitalfilter are given bydB p p )1(log 2010δα--=,dB s s )(log 2010δα-=.9.1 Preliminary ConsiderationsAs in the case of an analog lowpass filter, the specifications for a digital lowpass filter may alternatively be given in terms of its magnitude response, as in Figure 7.2. Here the maximum value of the magnitude in the passband is assumed to be unity, and the maximum passband deviation, denoted as 1/21ε+,is given by the minimum value of the magnitude in the passband. The maximum stopband magnitude is denoted by 1/A.For the normalized specification, the maximum value of the gain function or the minimum value of the loss function is therefore 0 dB. The quantity max α given bydB )1(log 20210max εα+=Is called the maximum passband attenuation. For p δ<<1, as is typically the case, it can be shown thatp p αδα2)21(l o g 2010max ≅--≅ The passband and stopband edge frequencies, in most applications, are specified in Hz, along with the sampling rate of the digital filter. Since all filter design techniques are developed in terms of normalized angular frequencies p ω and s ω,the sepcified criticalfrequencies need to be normalized before a specific filter design algorithm can be applied. Let T F denote the sampling frequency in Hz, and F P and F s denote, respectively,the passband and stopband edge frequencies in Hz. Then the normalized angular edge frequencies in radians are given byT F F F F p T p T p p ππω22==Ω=T F F F F s T s T s s ππω22==Ω= 9.1.2 Selection of the Filter TypeThe second issue of interest is the selection of the digital filter type,i.e.,whether an IIR or an FIR digital filter is to be employed. The objective of digital filter design is to develop a causal transfer function H(z) meeting the frequency response specifications. For IIR digital filter design, the IIR transfer function is a real rational function of 1-z. H(z)=N M dNz z d z d d pMz z p z p p ------++++++++ (22110221)10Moreover, H(z) must be a stable transfer function, and for reduced computational complexity, it must be of lowest order N. On the other hand, for FIR filter design, the FIRtransfer function is a polynomial in 1-z:∑=-=Nnnz nhzH] [)(For reduced computational complexity, the degree N of H(z) must be as small as possible. In addition, if a linear phase is desired, then the FIR filter coefficients must satisfy the constraint:][][Nnhnh-±=T here are several advantages in using an FIR filter, since it can be designed with exact linear phase and the filter structure is always stable with quantized filter coefficients. However, in most cases, the order N FIR of an FIR filter is considerably higher than the order N IIR of an equivalent IIR filter meeting the same magnitude specifications. In general, the implementation of the FIR filter requires approximately N FIR multiplications per output sample, whereas the IIR filter requires 2N IIR+1 multiplications per output sample. In the former case, if the FIR filter is designed with a linear phase, then the number of multiplications per output sample reduces to approximately (N FIR+1)/2. Likewise, most IIR filter designs result in transfer functions with zeros on the unit circle,and the cascade realization of an IIR filter of orderIIRN with all of the zeros on the unitcircle requires [(3IIRN+3)/2] multiplications per output sample. It has been shown that for most practical filter specifications, the ratio N FIR/N IIR is typically of the order of tens or more and, as a result, the IIR filter usually is computationally more efficient[Rab75]. However ,if the group delay of the IIR filter is equalized by cascading it with an allpass equalizer, then the savings in computation may no longer be that significant [Rab75]. In many applications, the linearity of the phase response of the digital filter is not an issue,making the IIR filter preferable because of the lower computational requirements.9.1.3 Basic Approaches to Digital Filter DesignIn the case of IIR filter design, the most common practice is to convert the digitalfilter specifications into analog lowpass prototype filter specifications, and then to transform it into the desired digital filter transfer function G(z). This approach has been widely used for many reasons:(a) Analog approximation techniques are highly advanced.(b) They usually yield closed-form solutions.(c) Extensive tables are available for analog filter design.(d) Many applications require the digital simulation of analog filters.In the sequel, we denote an analog transfer function as)()()(s D s P s H a a a =,Where the subscript "a" specifically indicates the analog domain. The digital transfer function derived form H a (s) is denoted by)()()(z D z P z G =The basic idea behind the conversion of an analog prototype transfer function H a (s) into a digital IIR transfer function G(z) is to apply a mapping from the s-domain to the z-domain so that the essential properties of the analog frequency response are preserved. The implies that the mapping function should be such that(a) The imaginary(j Ω) axis in the s-plane be mapped onto the circle of the z-plane.(b) A stable analog transfer function be transformed into a stable digital transfer function.To this end,the most widely used transformation is the bilinear transformation described in Section 9.2.Unlike IIR digital filter design,the FIR filter design does not have any connection with the design of analog filters. The design of FIR filter design does not have any connection with the design of analog filters. The design of FIR filters is therefore based on a direct approximation of the specified magnitude response,with the often addedrequirement that the phase response be linear. As pointed out in Eq.(7.10), a causal FIR transfer function H(z) of length N+1 is a polynomial in z -1 of degree N. The corresponding frequency response is given by∑=-=Nn n j j en h e H 0][)(ωω.It has been shown in Section 3.2.1 that any finite duration sequence x[n] of length N+1 is completely characterized by N+1 samples of its discrete-time Fourier transfer X(ωj e ). As a result, the design of an FIR filter of length N+1 may be accomplished by finding either the impulse response sequence {h[n]} or N+1 samples of its frequency response )H(e j ω. Also, to ensure a linear-phase design, the condition of Eq.(7.11) must be satisfied. Two direct approaches to the design of FIR filters are the windowed Fourier series approach and the frequency sampling approach. We describe the former approach in Section 7.6. The second approach is treated in Problem 7.6. In Section 7.7 we outline computer-based digital filter design methods.作者：Sanjit K.Mitra国籍：USA出处：Digital Signal Processing -A Computer-Based Approach 3eIIR数字滤波器的设计在一个数字滤波器发展的重要步骤是可实现的传递函数G（z）的接近给定的频率响应规格。
IIR数字滤波器英文文献以及翻译
2013 届毕业设计（论文）英文文献及其翻译资料院、部：电气与信息工程学院学生姓名：指导教师：职称专业：电子信息工程班级：完成时间：2013年6月7日Signal processingSignal processing is an area of electrical engineering and applied mathematics that deals with operations on or analysis of signals, in either discrete or continuous time, to perform useful operations on those signals. Signals of interest can include sound, images, time-varying measurement values and sensor data, for example biological data such as electrocardiograms, control system signals, telecommunication transmission signals such as radio signals, and many others. Signals are analog or digital electrical representations of time-varying or spatial-varying physical quantities. In the context of signal processing, arbitrary binary data streams and on-off signalling are not considered as signals, but only analog and digital signals that are representations of analog physical quantities.HistoryAccording to Alan V. Oppenheim and Ronald W. Schafer, the principles of signal processing can be found in the classical numerical analysis techniques of the 17th century. They further state that the "digitalization" or digital refinement of these techniques can be found in the digital control systems of the 1940s and 1950s.Categories of signal processingAnalog signal processingAnalog signal processing is for signals that have not been digitized, as in classical radio, telephone, radar, and television systems. This involves linear electronic circuits such as passive filters, active filters, additive mixers, integrators and delay lines. It also involves non-linear circuits such as compandors, multiplicators (frequency mixers and voltage-controlled amplifiers), voltage-controlled filters, voltage-controlled oscillators and phase-locked loops.Discrete time signal processingDiscrete time signal processing is for sampled signals that are considered as defined only at discrete points in time, and as such are quantized in time, but not in magnitude.Analog discrete-time signal processing is a technology based on electronic devices such as sample and hold circuits, analog time-division multiplexers, analog delay lines and analog feedback shift registers. This technology was a predecessor of digital signal processing (see below), and is still used in advanced processing of gigahertz signals.The concept of discrete-time signal processing also refers to a theoretical discipline that establishes a mathematical basis for digital signal processing, without taking quantization error into consideration.Digital signal processingDigital signal processing is for signals that have been digitized. Processing is done by general-purpose computers or by digital circuits such as ASICs, field-programmable gate arrays or specialized digital signal processors (DSP chips). Typical arithmetical operations include fixed-point and floating-point, real-valued and complex-valued, multiplication and addition. Other typical operations supported by the hardware are circular buffers and look-up tables. Examples of algorithms are the Fast Fourier transform (FFT), finite impulse response (FIR) filter, Infinite impulse response (IIR) filter, and adaptive filters such as the Wiener and Kalman filters1.Digital signal processingDigital signal processing (DSP) is concerned with the representation of signals by a sequence of numbers or symbols and the processing of these signals. Digital signal processing and analog signal processing are subfields of signal processing. DSP includes subfields like: audio and speech signal processing, sonar and radar signal processing, sensor array processing, spectral estimation, statistical signal processing, digital image processing, signal processing for communications, control of systems, biomedical signal processing, seismic data processing, etc.The goal of DSP is usually to measure, filter and/or compress continuous real-world analog signals. The first step is usually to convert the signal from an analog to a digital form, by sampling it using an analog-to-digital converter (ADC), which turns the analog signal into a stream of numbers. However, often, the required output signal is another analog output signal, which requires a digital-to-analogconverter (DAC). Even if this process is more complex than analog processing and has a discrete value range, the application of computational power to digital signal processing allows for many advantages over analog processing in many applications, such as error detection and correction in transmission as well as data compression.[1]DSP algorithms have long been run on standard computers, on specialized processors called digital signal processors (DSPs), or on purpose-built hardware such as application-specific integrated circuit (ASICs). Today there are additional technologies used for digital signal processing including more powerful general purpose microprocessors, field-programmable gate arrays (FPGAs), digital signal controllers (mostly for industrial apps such as motor control), and stream processors, among others.[2]2. DSP domainsIn DSP, engineers usually study digital signals in one of the following domains: time domain (one-dimensional signals), spatial domain (multidimensional signals), frequency domain, autocorrelation domain, and wavelet domains. They choose the domain in which to process a signal by making an informed guess (or by trying different possibilities) as to which domain best represents the essential characteristics of the signal. A sequence of samples from a measuring device produces a time or spatial domain representation, whereas a discrete Fourier transform produces the frequency domain information, that is the frequency spectrum. Autocorrelation is defined as the cross-correlation of the signal with itself over varying intervals of time or space.3. Signal samplingMain article: Sampling (signal processing)With the increasing use of computers the usage of and need for digital signal processing has increased. In order to use an analog signal on a computer it must be digitized with an analog-to-digital converter. Sampling is usually carried out in two stages, discretization and quantization. In the discretization stage, the space of signals is partitioned into equivalence classes and quantization is carried out by replacing the signal with representative signal of the corresponding equivalence class. In thequantization stage the representative signal values are approximated by values from a finite set.The Nyquist–Shannon sampling theorem states that a signal can be exactly reconstructed from its samples if the sampling frequency is greater than twice the highest frequency of the signal; but requires an infinite number of samples . In practice, the sampling frequency is often significantly more than twice that required by the signal's limited bandwidth.A digital-to-analog converter is used to convert the digital signal back to analog. The use of a digital computer is a key ingredient in digital control systems.4. Time and space domainsMain article: Time domainThe most common processing approach in the time or space domain is enhancement of the input signal through a method called filtering. Digital filtering generally consists of some linear transformation of a number of surrounding samples around the current sample of the input or output signal. There are various ways to characterize filters; for example:∙ A "linear" filter is a linear transformation of input samples; other filters are "non-linear". Linear filters satisfy the superposition condition, i.e. if an input is a weighted linear combination of different signals, the output is an equally weighted linear combination of the corresponding output signals.∙ A "causal" filter uses only previous samples of the input or output signals; while a "non-causal" filter uses future input samples. A non-causal filter can usually be changed into a causal filter by adding a delay to it.∙ A "time-invariant" filter has constant properties over time; other filters such as adaptive filters change in time.∙Some filters are "stable", others are "unstable". A stable filter produces an output that converges to a constant value with time, or remains bounded within a finite interval. An unstable filter can produce an output that grows without bounds, with bounded or even zero input.A "finite impulse response" (FIR) filter uses only the input signals, while an "infinite impulse response" filter (IIR) uses both the input signal and previous samples of the output signal. FIR filters are always stable, while IIR filters may be unstable.Filters can be represented by block diagrams which can then be used to derive a sample processing algorithm to implement the filter using hardware instructions. A filter may also be described as a difference equation, a collection of zeroes and poles or, if it is an FIR filter, an impulse response or step response.The output of a digital filter to any given input may be calculated by convolving the input signal with the impulse response.5. Frequency domainMain article: Frequency domainSignals are converted from time or space domain to the frequency domain usually through the Fourier transform. The Fourier transform converts the signal information to a magnitude and phase component of each frequency. Often the Fourier transform is converted to the power spectrum, which is the magnitude of each frequency component squared.The most common purpose for analysis of signals in the frequency domain is analysis of signal properties. The engineer can study the spectrum to determine which frequencies are present in the input signal and which are missing.In addition to frequency information, phase information is often needed. This can be obtained from the Fourier transform. With some applications, how the phase varies with frequency can be a significant consideration.Filtering, particularly in non-realtime work can also be achieved by converting to the frequency domain, applying the filter and then converting back to the time domain. This is a fast, O(n log n) operation, and can give essentially any filter shape including excellent approximations to brickwall filters.There are some commonly used frequency domain transformations. For example, the cepstrum converts a signal to the frequency domain through Fourier transform, takes the logarithm, then applies another Fourier transform. This emphasizes thefrequency components with smaller magnitude while retaining the order of magnitudes of frequency components.6. Z-domain analysisWhereas analog filters are usually analysed on the s-plane; digital filters are analysed on the z-plane or z-domain in terms of z-transforms.Most filters can be described in Z-domain (a complex number superset of the frequency domain) by their transfer functions. A filter may be analysed in the z-domain by its characteristic collection of zeroes and poles.7. ApplicationsThe main applications of DSP are audio signal processing, audio compression, digital image processing, video compression, speech processing, speech recognition, digital communications, RADAR, SONAR, seismology, and biomedicine. Specific examples are speech compression and transmission in digital mobile phones, room matching equalization of sound in Hifi and sound reinforcement applications, weather forecasting, economic forecasting, seismic data processing, analysis and control of industrial processes, computer-generated animations in movies, medical imaging such as CAT scans and MRI, MP3 compression, image manipulation, high fidelity loudspeaker crossovers and equalization, and audio effects for use with electric guitar amplifiers8. ImplementationDigital signal processing is often implemented using specialised microprocessors such as the DSP56000, the TMS320, or the SHARC. These often process data using fixed-point arithmetic, although some versions are available which use floating point arithmetic and are more powerful. For faster applications FPGAs[3]might be used. Beginning in 2007, multicore implementations of DSPs have started to emerge from companies including Freescale and Stream Processors, Inc. For faster applications with vast usage, ASICs might be designed specifically. For slow applications, a traditional slower processor such as a microcontroller may be adequate. Also a growing number of DSP applications are now being implemented on Embedded Systems using powerful PCs with a Multi-core processor.信号处理信号处理是电气工程和应用数学领域，在离散的或连续的时间域处理和分析信号，以对这些信号进行所需的有用的操作。
基于MATLAB的IIR滤波器的设计
基于MATLAB的IIR滤波器的设计IIR (Infinite Impulse Response) 滤波器是一种数字滤波器，由其无限长的冲激响应函数所定义。
MATLAB中提供了强大而灵活的工具来设计和实现IIR滤波器。
在本文中，我们将探讨基于MATLAB的IIR滤波器设计的原理、步骤以及一些常见的应用实例。
IIR滤波器设计的原理：IIR滤波器设计的基本原理是将滤波器的传递函数表示为分子多项式和分母多项式的比值。
分母多项式是滤波器的极点，分子多项式是滤波器的零点。
通过选择合适的极点和零点，可以实现不同的滤波特性，如低通滤波、高通滤波、带通滤波等。
MATLAB中的IIR滤波器设计步骤：1.确定所需滤波器的规格：确定滤波器的类型（低通、高通、带通等），截止频率，衰减等级等。
2. 设计滤波器的理想传递函数：根据滤波器的规格，使用MATLAB中的相应函数（例如，butter、cheby1、cheby2等）设计滤波器的理想传递函数。
3. 转换理想传递函数为一阶和二阶部分：使用MATLAB中的函数（例如，tf2sos、zpk2sos等）将理想传递函数转换为一阶和二阶部分。
4.选择滤波器的实现方式：根据设计要求，选择IIR滤波器的直接形式、传输形式或级联形式等实现方式。
5. 将设计好的IIR滤波器进行实现：使用MATLAB中的函数（例如，filter、dfilt）来实现设计好的IIR滤波器。
IIR滤波器设计的应用实例：1.语音信号处理：IIR滤波器在语音信号处理中广泛应用，可以提取语音信号中的特定频率成分，如去除噪声、语音增强等。
2.图像处理：IIR滤波器可用于图像处理中的边缘检测、平滑处理、锐化处理等。
3.生物医学信号处理：IIR滤波器在生物医学信号处理中常用于心电图（ECG）滤波、脑电图（EEG）滤波等。
4.控制系统：IIR滤波器可以用于控制系统中的数模转换、滤波、模拟信号转数字信号等。
总结：MATLAB提供了强大而灵活的工具来设计和实现IIR滤波器。
基于Matlab的IIR数字滤波器设计(论文)之令狐文艳创作
摘要令狐文艳在现代通信系统中，由于信号中经常混有各种复杂成分，所以很多信号分析都是基于滤波器而进行的，而数字滤波器是通过数值运算实现滤波，具有处理精度高、稳定、灵活、不存在阻抗匹配问题，可以实现模拟滤波器无法实现的特殊滤波功能。
数字滤波器根据其冲激响应函数的时域特性，可分为两种，即无限长冲激响应(IIR)数字滤波器和有限长冲激响应(FIR)数字滤波器。
实现IIR滤波器的阶次较低，所用的存储单元较少，效率高，精度高，而且能够保留一些模拟滤波器的优良特性，因此应用很广。
Matlab软件以矩阵运算为基础，把计算、可视化及程序设计有机融合到交互式工作环境中，并且为数字滤波的研究和应用提供了一个直观、高效、便捷的利器。
尤其是Matlab中的信号处理工具箱使各个领域的研究人员可以直观方便地进行科学研究与工程应用。
本文首先介绍了数字滤波器的概念，分类以及设计要求。
接着利用MATLAB函数语言编程，用信号处理图形界面FDATool来设计滤波器以及Sptool界面设计的方法，并用FDATool模拟IIR数字滤波器处理信号。
重点设计Chebyshev I型和Chebyshev II型数字低通滤波器,并介绍最优化设计。
【关键字】IIR滤波器FDAToolSptoolSimulinkABSTRACTIn modern communication systems,Because oftenmixed with various signal complex components,So many signal analysis is based on filters, and the digital filter is realized through numerical computation, digital filters filter with high precision, stability and flexibility, don't exist, can realize the impedance matching simulating the special filter cannot achieve filter function. Digital filter according to its impulse response function and characteristics of the time can be divided into two kinds, namely the infinite impulse response (IIR) digital filter and finite impulse response (FIR digital filters). The order of realizing IIR filter is used, low and high efficiency less storage unit, high precision, and can keep some simulation characteristics of filter, so it is widely used. Matlab software based on matrix computation, the calculation, visualization and program design of organic integration to interactiveenvironment for digital filter, and the research and application of provides an intuitive, efficient and convenient tool. Especially in the Matlab signal processing to all areas of research toolbox personnel can easily for scientific research and engineering application. This paper introduces the concept of digital filter,classification and design requirements. Then using MATLAB language programming, with functions of signal processing FDATool graphical interface design of interface design and Sptool filter, and FDATool analog signal processing IIR digital filter. Key design Chebyshev type I and II digital Chebyshev lowpass filter, and introduces optimization design.【Keywords】 IIR Filter FDATool Sptool Simulink目录前言１第一章数字滤波器２第一节数字滤波器的概念２第二节数字滤波器的分类２第三节数字滤波器的设计要求４第二章 IIR数字滤波器设计方法５第一节 IIR数字滤波器的设计步骤５第二节用脉冲相应不变法设计IIR数字滤波器６一、设计原理６二、脉冲响应不变法优缺点８第三节双线性变换法设计IIR数字滤波器９一、设计原理９二、双线性变换法优缺点１１第三章 IIR滤波器的MATLAB设计１３第一节 IIR数字滤波器的典型设计法１４第二节 IIR数字滤波器的直接设计法１８第三节 FDATool介绍和界面设计２３第四节 FDATOOL设计IIR数字滤波器２４第五节 SIMULINK 仿真IIR滤波器２６总结２９致谢３０参考文献３１结束语３２前言随着信息时代和数字世界的到来，数字信号处理已成为当今一门极其重要的学科和技术领域。