差分演进算法TDOA定位

摘要

无线定位服务是一种有着广阔市场前景的移动增值业务，基本原理是利用现有蜂窝网络，通过对各种位置特征参数，包括到达时间(TOA)、到达时间差(TDOA)、到达方向(DOA)的测量和估计，来实现移动用户的定位。本论文对无线通信网络中基于TDOA的无线定位技术进行了研究。

本文分析了国内外相关研究现状，给出了移动台定位的几种基本方法，并给出了TDOA定位的双曲线数学模型，分析了基于TDOA定位的Chan算法、遗传算法(GA)和差分演进算法(DE)，并对其进行了计算机仿真。仿真结果表明，三种算法各有优缺点：Chan算法定位精度较低但运算速度很快，GA算法和DE算法定位精度高但收敛时间较长。

在上述研究的基础上，本论文提出了三种新的定位算法：基于TDOA的Chan-GA算法、Chan-DE算法和Chan-IDE算法。并在相同的仿真环境下进行比较，仿真结果表明，在保证种群数量的情况下，所提的算法性能稳定，能找到逼近全局最优点的解，相对于Chan算法精度更高,相对于以前的算法在保证收敛性能的前提下有更快的收敛速度。

关键词：移动台定位；到达时间差；遗传算法；差分演进算法；免疫算法

ABSTRACT

Cellular wireless location service is a new mobile value-added service with a good market future. Its basic principle is to implement mobile user location through estimating characteristic parameters relative to position, including time-of-arrival (TOA), time-difference-of-arrival (TDOA), direction-of-arrival (DOA), etc. This thesis aims at the research of wireless location technology based on time-related measurements in Wireless Communication System.

The thesis analyzes the domestic and foreign correlation research of present situation, and gives several essential methods of mobile location. After that, the mathematical model of TDOA hyperbolic equations is established, three location algorithms based on time-difference-of-arrival (TDOA), Chan, genetic algorithm and Differential Evolution are analyzed, and have been carried on the simulation to them. The simulation results show that all the algorithms have the advantages and disadvantages.The Chan algorithm has bad location accuracy and very quick operating speed. To the contrary, the genetic algorithm and Differential Evolution have a high accuracy and a fast convergence time.

Based on the above investigation, three new location algorithms called Chan-GA algorithm, Chan-DE algorithm and Chan-IDE algorithm based on TDOA measurements are put forward. Carrying on the computer simulation to them under the same environment, the simulation results show that if the population size is big enough, the algorithm is robust and can find the coordinates. It has a higher accuracy than Chan algorithms and a faster convergence time than genetic algorithm.

Key words: Mobile location; TDOA; Genetic algorithm; Differential Evolution; Immune algorithm

目录

第1章绪论 (1)

1.1课题研究背景 (1)

1.2课题研究的目的和意义 (2)

1.3国内外的研究现状 (4)

1.4本文的主要工作 (5)

第2章移动台定位的基本方法 (7)

2.1移动台定位的两种方案 (7)

2.1.1基于网络的定位 (7)

2.1.2基于移动台的定位 (7)

2.2移动台定位技术 (8)

2.2.1基于场强测量的定位方法 (8)

2.2.2基于传播时间测量的定位方法 (8)

2.2.3基于信号到达角度测量的定位方法 (10)

2.2.4混合定位方法 (10)

2.3影响移动台定位精度的主要原因 (11)

2.4本章小结 (12)

第3章基于TDOA定位算法的分析及仿真 (13)

3.1TDOA定位的数学模型 (13)

3.1.1定位问题的最小二乘(LS)表示 (13)

3.1.2TDOA双曲线模型 (14)

3.2TDOA定位算法——Chan算法 (15)

3.3定位准确率的评价指标 (20)

3.4本章小结 (21)

第4章遗传算法在TDOA定位中的应用 (22)

4.1遗传算法简介 (22)