智能家居室内感应定位系统中英文对照外文翻译文献

智能家居室内感应定位系统中英文对照外文翻译文献(文档含英文原文和中文翻译)
A Pyroelectric Infrared Sensor-based Indoor Location-Aware
System for the Smart Home
Suk Lee, Member, IEEE, Kyoung Nam Ha, Kyung Chang Lee, Member, IEEE Abstract —Smart home is expected to offer various intelligent services by recognizing residents along with their life style and feelings. One of the key issues for realizing the smart home is how to detect the locations of residents. Currently, the research effort is focused on two approaches: terminal-based and non-terminal-based methods. The terminal -based method employs a type of device that should be carried by the resident while the non-terminal-based method requires no such device. This paper presents a novel non-terminal-based approach using
an array of pyroelectric infrared sensors (PIR sensors) that can detect residents. The feasibility of the system is evaluated experimentally on a test bed
Index Terms— smart home, location-based service, pyroelectric infrared sensor (PIR sensor), location-recognition algorithm
I. INTRODUCTION
There is a growing interest in smart home as a way to offer a convenient, comfortable, and safe residential environment [1], [2]. In general, the smart home aims to offer appropriate intelligent services to actively assist i n the resident’s life such as housework, amusement, rest, and sleep. Hence, in order to enhance the resident’s convenience and safety, devices such as home appliances, multimedia appliances, and internet appliances should be connected via a home network system, as shown in Fig. 1, and they should be controlled or monitored remotely using a television (TV) or personal digital assistant (PDA) [3], [4].
Fig. 1. Architecture of the home network system for smart home Especially, attention has been focused on location-based services as a way to offer high-quality intelligent services, while considering human factors such as pattern of living, health, and feelings of a resident [5]-[7]. That is, if the smart home can recognize the resident’s pattern of living o r health, then home appliances should be able to anticipate the resident’s needs and offer appropriate intelligent service more actively. For example, in a passive service environment, the resident controls the operation of the HVAC (heating, ventilating, and air conditioning) system, while the smart home would control the temperature and humidity of a room according to the resident’s condition. Various indoor location-aware systems have been
developed to recognize the resident’s location in the smart home or smart office. In general, indoor location-aware systems have been classified into three types according to the measurement technology: triangulation, scene analysis, and proximity methods [8]. The triangulation method uses multiple distances from multiple known points. Examples include Active Badges [9], Active Bats [10], and Easy Living [11], which use infrared sensors, ultrasonic sensors, and vision sensors, respectively. The scene analysis method examines a view from a particular vantage point. Representative examples of the scene analysis method are MotionStar [12], which uses a DC magnetic tracker, and RADAR [13], which uses IEEE 802.11 wireless local area network (LAN). Finally, the proximity method measures nearness to a known set of points. An example of the proximity method is Smart Floor [14], which uses pressure sensors. Alternatively, indoor location-aware systems can be classified according to the need for a terminal that should be carried by the resident. Terminal-based methods, such as Active Bats, do not recognize the resident’s location directly, but perceive the location of a device carried by the resident, such as an infrared transceiver or radio frequency identification (RFID) tag. Therefore, it is impossible to recognize the resident’s location if he or she is not carrying the device. In contrast, non-terminal methods such as Easy Living and Smart Floor can find the resident’s location without such devices. However, Easy Living can be regarded to invade the resident’s privacy while the Smart Floor has difficulty with extendibility and maintenance. This paper presents a non-terminal based location-aware system that uses an array of pyroelectric infrared (PIR) sensors [15], [16]. The PIR sensors on the ceiling detect the presence of a resident and are laid out so that detection areas of adjacent sensors overlap. By combining the outputs of multiple PIR sensors, the system is able to locate a resident with a reasonable degree of accuracy. This system has inherent advantage of non-terminal based methods while avoiding privacy and extendibility, maintenance issues. In order to demonstrate its efficacy, an experimental test bed has been constructed, and the proposed system has been evaluated experimentally under various experimental conditions. This paper is organized into four sections, including this introduction. Section II presents the architecture of the PIR sensor-based indoor location-aware system (PILAS), and the location-recognition algorithm. Section III describes a resident-detection method using PIR sensors, and evaluates the performance of the system under various conditions using an experimental test bed. Finally, a summary and the conclusions are presented in Section IV.
II. ARCHITECTURE OF THE PIR SENSOR-BASED INDOOR
LOCATION-AWARE SYSTEM
A. Framework of the smart home
Given the indoor environment of the smart home, an indoor location-aware system must satisfy the following requirements. First, the location-aware system should be implemented at a relatively low cost because many sensors have to be installed in rooms of different sizes to detect the resident in the smart home. Second, sensor installation must be flexible because the shape of each room is different and there are obstacles such as home appliances and furniture, which prevent the normal operation of sensors. The third requirement is that the sensors for the location-aware system have to be robust to noise, and should not be affected by their surroundings. This is because the smart home can make use of various wireless communication methods such as wireless LAN or radio-frequency (RF) systems, which produce electromagnetic noise, or there may be significant changes in light or temperature that can affect sensor performance. Finally, it is desirable that the system’s accuracy is adjustable according to room types.
Among many systems that satisfy the requirement, the PIR sensor-based system has not attracted much attention even though the system has several advantages. The PIR sensors,which have been used to turn on a light when it detects human movement, are less expensive than many other sensors. In addition, because PIR sensors detect the infrared wavelengthemitted from humans between 9.4~10.4 μm, they are reasonably robust to their surroundings, in terms of temperature, humidity, and electromagnetic noise. Moreover, it ispossible to control the location accuracy of the system by adjusting the sensing radius of a PIR sensor, and PIR sensors are easily installed on the ceiling, where they are not affected by the structure of a room or any obstacles.
Figure 2 shows the framework for the PILAS in a smart home that offers location-based intelligent services to a resident. Within this framework, various devices are connected via a home network system, including PIR sensors, room terminals, a smart home server, and home appliances. Here, each room is regarded as a cell, and the appropriate number of PIR sensors is installed on the ceiling of each cell to provide sufficient location accuracy for the location-based services. Each PIR sensor attempts to detect the resident at a constant period, and transmits its sensing information to a room terminal via the home network system.
Fig. 2. Framework of smart home for the PILAS.
Consequently, the room terminal recognizes the resident’s l ocation by integrating the sensor information received from all of the sensors belonging to one cell, and transmits the resident’s location to the smart home server that controls the home appliances to offer location-based intelligent services to the resident.
Within this framework, the smart home server has the following functions. 1) The virtual map generator makes a virtual map of the smart home (generating a virtual map), and writes the location information of the resident, which is received from a room terminal, on the virtual map (writing the resident’s location). Then, it makes a moving trajectory of the resident by connecting the successive locations of the resident (tracking the resident’s movement). 2) The home appliance controller transmits control commands to home appliances via the home network system to provide intelligent services to the resident. 3) The moving pattern predictor saves the current movement trajectory of the resident, the current action of home appliances, and parameters reflecting the current home environment such as the time, temperature, humidity, and illumination. After storing sufficient information, it may be possible to offer human-oriented intelligent services in which the home appliances spontaneously provide services to satisfy human needs. For example, if the smart home server “knows” that the resident normally wakes up at 7:00 A.M. and takes a shower, it may be possible to turn on the lamps and some music. In addition, the temperature of the shower water can be set automatically for the resident.
B. Location-recognition algorithm
In order to determine the location of a resident within a room, an array of PIR sensors are used as shown in Fig. 3. In the figure, the sensing area of each PIR sensor is shown as a circle, and the sensing areas of two or more sensors overlap. Consequently, when a resident enters one of the sensing areas, the system decides whether he/she belongs to any sensing area by integrating the sensing information collected from all of the PIR sensors in the room. For example, when a resident enters the sensing area B, sensors a and b output ‘ON’ signals, while sensor c outputs ‘OFF’ signal. After collecting outputs, the algorithm can infer that the resident belongs to the sensing area B. According to the number of sensors and the arrangement of the sensors signaling ‘ON’, the resident’s location is deter-mined in the following manner. First, if only one sensor outputs ‘ON’ signal, the resident is regarded to be at the center of the sensing area of the corre sponding sensor. If the outputs of two adjacent sensors are ‘ON’, the resident’s location is assumed to be at the point midway between the two sensors. Finally, if three or more sensors signal ‘ON’, the resident is located at the centroid of the centers of the corresponding sensors. For example, it is assumed that the resident is located at point 1 in the figure when only sensor a signals ‘ON’, while the resident is located at point 2 when sensors a and b both output ‘ON’ signals.
The location accuracy of this system can be defined the maximum distance between the estimated points and the resident. For example, when a resident enters sensing area A, the resident is assumed to be at point 1. On the assumption that a resident can be represented by a point and the radius of the sensing area of a PIR sensor is 1 m, we know that the location accuracy is 1 m because the maximum error occurs when the resident is on the boundary of sensing area A. Alternatively, when the resident is in sensing area B, the resident is assumed to be at point 2, and the maximum location error occurs when the resident is actually at point 3. In this case, the error is 3 / 2 m which is the distance between points 2 and 3. Therefore, the location accuracy of the total system shown in Fig. 3 can be regarded as 1 m, which is the maximum value of the location accuracy of each area. Since the number of sensors and the size of their sensing areas determine the location accuracy of the PILAS, it is necessary to arrange the PIR sensors properly to guarantee the specified system accuracy.
Fig. 3. The location-recognition algorithm for PIR sensors.
In order to determine the resident’s location precisely and increase the accuracy of the system, it is desirable to have more sensing areas with given number of sensors and to have sensing areas of similar size. Fig. 4 shows some examples of sensor arrangements and sensing areas. Fig. 4(a) and 4(b) show the arrangements with nine sensors that produce 40 and 21 sensing areas, respectively. The arrangement in Fig. 4(a) is better than Fig. 4(b) in terms if the number of sensing areas. However, the arrangement in Fig. 4(a) has some areas where a resident can not be detected and lower location accuracy than that in Fig. 4(b). Fig. 4(c) shows an arrangement with twelve sensors that five 28 sensing areas without any blind spots.
Fig. 4. Location accuracy according to the sensor arrangement of PIR
sensors. (a) 40 sensing areas. (b) 21 sensing areas. (c) 28 sensing areas
with twelve sensors.
When PIR sensors are installed around the edge of a room, as shown in Fig. 4(c), it sometimes may give awkward results. One example is shown in Fig. 5. Fig. 5(a) shows the path of a resident. If we mark the estimated points by using the sensor location or the midpoint of adjacent sensors, it will be a zigzagging patterns as shown in Fig. 5(b). In order to alleviate this, we may regard the sensors on the edges to be located a little inwards, which give the result shown in Fig. 5(c).
Fig. 5. The effect of compensating for the center point of the outer sensors.
(a) Resident’s movement. (b) Before compensating for the outer sensors. (c)
After compensating for the outer sensors.
III. PERFORMANCE EVALUATION OF THE PILAS
A. Resident-detection method using PIR sensors
Since the PILAS recognizes the resident’s location by combining outputs from all the sensors belonging to one cell, determining whether a single sensor is ‘ON’ or ‘OFF’ directly influences location accuracy. In general, because the ‘ON/OFF’ values can be determined by co mparing a predefined threshold and the digitized sensor output acquired by sampling the analog signal from a PIR sensor, it is necessary to choose an appropriate signal level for the threshold. For example, Smart Floor, which is another non-terminal method, can recognize a resident’s location exactly by comparing the appropriate threshold and a sensor value, because a pressure sensor outputs a constant voltage based on the resident’s weight when he remains at a specific point. However, because a PIR sensor measures the variation in the infrared signal produced by a moving human body, its output is in analog form, as shown in Fig. 6. That is, as the variation in the infrared
radiation from a resident increases when a resident enters a sensing area, the PIR sensor outputs an increasing voltage. Conversely, the voltage decreases as the resident leave the sensing area. If the resident does not move within the sensing area, the variation in the infrared radiation does not exist and the PIR sensor outputs zero voltage. Therefore, it is very difficult to deter-mine when a resident is staying resident within a specific sensing area using only the voltage or current threshold of a PIR sensor.
Fig. 6. Signal output of PIR sensor.
In order to guarantee the location accuracy of the system, the resident-detection method must meet several requirements. First, if no resident is present within a sensing area, the PIR sensor should not output ‘ON’ signal. That is, the PIR sensor must not malfunction by other disturbances such as a moving pet, temperature change and sunlight. Second, it should be possible to precisely determine the point in time when a resident enters and leaves a sensing area. That is, in spite of variations in sensor characteristics, resident’s speed and heig ht, it should be possible to determine the time point exactly. Finally, because the output voltage of a PIR sensor does not exceed the threshold voltage when the resident does not move within a sensing area, it is necessary to know if a resident stays within the sensing area.
In order to satisfy these requirements, this paper introduces the following implementation method for the resident detection method for PIR sensors. First, in order to eliminate PIR sensor malfunctioning due to pets or temperature changes, a Fresnel lens, which allows human infrared waveforms to pass through it while rejecting other waveforms, is installed in front of the PIR sensors. Second, when the output of a PIR sensor exceeds the positive threshold voltage, and this state is maintained for several predefined sampling intervals, that the resident has entered a sensing area. Here, the threshold must be sufficient for the method to distinguish variation in the resident’s infrared from an environmental infrared signal caused by pets o r temperature change. Moreover, when the sensor’s output falls below a negative threshold voltage and this status is maintained for several sampling intervals, it is assumed that the resident has left the sensing area. Finally, when the output voltage remains between the two threshold voltages, for example when the resident is not moving inside the sensing area, the output of the corresponding PIR sensor is changed from ‘ON’ to ‘OFF’. At this time, if other sensors installed near this sensor do not
output ‘ON’ signal, the method regards the resident as remaining within the corresponding sensing area.
B. Performance evaluation using an experimental test bed
In order to verify the feasibility of the PILAS, an experimental test bed was implemented. Since the intelligent location-based service in the smart home does not require very high location accuracy, we designed the system to have a location accuracy of 0.5 m. Figure 7 shows the experimental test bed in a room measuring 4 ×4 ×2.5 m (width ×length ×height). In the experiment, twelve PIR sensors were fixed on the ceiling, using the arrangement shown in Fig. 4(c). An Atmel AT89C51CC001 microcontroller [17] was used for signal processing and judging ‘ON/OFF’, and a Nippon Ceramic RE431B PIR sensor [18] and N L-11 Fresnel lens were used. Especially, a horn was installed on each PIR sensor to limit the sensing area to the circle with 2 m diameter. Fig. 8 shows the experimental results with the horn. In the figure, the RE431B sensor outputs the signal shown in (a) when a resident passes through the sensing circle, while it outputs the irregular signal shown in (b) when the resident moves within the circle. Finally, no signal is detected when the resident moves outside the circle, as shown in (c). From these experimental results, we verified that the PIR sensor detects residents within the sensing area only. In addition, in order to judge whether the signal is ‘ON’ or ‘OFF’, it is necessary to choose a threshold for the RE431B sensor that considers external environmental disturbance. Initially, several experiments were performed to determine the threshold with respect to the internal temperature change caused by a air conditioner or heater and other disturbances, such as wind or sunshine. Based on these experimental results, when the threshold of the RE431B sensor was ±0.4 V, external environmental temperature change did not affect its performance at detecting the resident. In addition, we verified that pets did not affect the sensing performance with the same threshold.
Fig. 7. Experimental test bed for the PILAS.
Fig. 8. Ensuring the exact sensing range with a horn.
Next, in order to determine the resident’s location using the information received from PIR sensors, a PC-based locationrecognition algorithm was implemented, as shown in Fig. 9. Here, a PC collects data from the PIR sensors every 10 msec using an NI 6025E data acquisition (DAQ) board [19]. In the figure, the line in the left window was drawn using a mouse to show the path of the resident graphically, while that in the window on the right is the estimated movement trajectory of the resident drawn by connecting the resident’s locations acquired using the DAQ board.
Finally, in order to verify the efficacy of the system, three experiments were performed with residents between 160 and 180 cm tall, moving at speeds between 1.5 and 2.5 km/h. Figure 9 shows the trajectory of a resident moving along a Tshaped path. The trajectory made by connecting the resi-dent’s locations recognized by the PILAS, shown on th e right, was similar to the target path shown on the left. We know that the maximum location error is about 30 cm without compensating for the outer sensors. Fig. 10 shows the trajectory when the resident follows an H-shaped path. In this experiment, the location accuracy was similar to that in Fig. 9. We verified that the system could locate a resident with accuracy of 0.5 m, even if three or more sensors were activated. Figure 11 shows the trajectory of a resident moving along a square path. In this case, the location error is the largest, and the trajectory is not a straight line. We note that serious location errors occurred at each point marked by A due to the inaccurate judgment of the outer sensors. Nevertheless, the location error is still smaller than 0.5 m when moving in the square path. Here, the compensation method for outer sensors, which was explained in Fig. 5, reduces the location error at each point A. When the resident moves in a straight line, as shown in Fig. 12(a), the location error is relatively large without using the compensation method, as shown in Fig. 12(b). However, after applying the compensation method, we verified that the detection results for the areas in the small circles are enhanced by roughly about 30%.
IV. SUMMARY AND CONCLUSIONS
This paper presents a PIR sensor-based indoor location aware system that estimates the resident’s location for location-based intelligent services in the smart home. This paper introduces the framework of smart home for the location-aware system, and a location-recognition algorithm that integrates the information collected from PIR sensors. In addition, this paper presents a resident-detection method. Finally, an experiment is implemented to evaluate the efficacy of the PILAS.
Based on several experiments conducted under various conditions, we verified that the PILAS can estimates resident’s location sufficiently well. Moreover, because the location accuracy of the system is less than 0.5 m without any terminal for location recognition, the system can be very practical. Furthermore, it should be possible to enhance the location accuracy of the system by increasing the number of sensing areas, by equalizing the sensing areas based on the sensor arrangement, or by compensating for the centers of outer sensors.
Since the location accuracy of this system differs according to the sensor arrangement, it is necessary to determine the optimal sensor arrangement that offers the greatest location accuracy. In order to enhance the location accuracy, it is also necessary to enhance the method of processing the PIR sensors using more advanced techniques such as probabilistic theories and soft computing. Finally, the proposed PILA system should be extended to deal with a room occupied by more than one residents.
基于热释电红外传感器的智能家居室内感应定位系统
Suk Lee，电机及电子学工程师联合会会员
Kyoung Nam Ha, Kyung Chang Lee,电机及电子学工程师联合会会员
摘要——智能家居，是一种可以通过识别具有不同生活习惯和感觉的住户来提供各种不同的智能服务。

而实现这样的功能其中最关键的问题之一就是如何确定住户的位置。

目前，研究工作只要集中于两种方法：终端方式和非终端方式。

终端方式需要一种住户随身携带的设备，而非终端方式则不需要这样的设备。

本文提出一种使用可以探测到住户的热释电红外传感器（红外传感器）的新的非终端方式。

该系统的可行性已经通过了测试平台的实验性评估。

索引词——智能家居，定位服务，热释电红外传感器（红外传感器），定位识别算法
I. 简介
现在由于人人都想有一个方便，舒适，安全的居住环境，因此大家对于智能家居表现的越来越感兴趣[1] [2]。

一般来说，智能家居旨在提供合适的智能服务来积极促进住户更好的生活，比如家务劳动，娱乐，休息和睡眠。

因此，为了提高住户的便捷和安全，像家用电器，多媒体设备和互联网设备应通过家庭网络系统连接在一起，如图1所示。

并且它们应通过电视或个人数字助理（PDA）来控制或远程监控[3] [4]。

图1 智能家居的家庭网络体系结构
尤其要注意的是，作为一种提供高质量的智能服务，目标应集中于定位服务，同时考虑人为因素，比如住户的生活方式，健康状况和居住感受[5]—[7]。

也就是说，如果智能家居能识别住户的生活方式或健康状况，那么家用电器应该能预见住户的需要，并能更主动的提供适合的智能服务。

例如，在一个被动的服务环境下，需要住户控制供热通风与空气调节系统（供暖，通风和空调），而智能家居将根据住
户情况自动调节房间的温湿度。

智能家居或智能办公室的各种室内感应定位系统的已经研发到能够识别住户的位置。

一般来说，室内定位感应系统根据测量技术分为三种类型：三角测量，场景分析和接近方法[8]。

三角测量法是通过多个已知点来计算位置距离。

运用三角测量法的例子包括Active Badges[9]，Active Bats[10]和Easy Living[11]，它们分别运用了红外传感器，超声波传感器和视觉传感器来实现的。

场景解析法是检测一个场景内的特定着眼点。

场景解析法的典型例子是使用直流磁力跟踪器的MotiveStar[12]，和使用无线局域网络[LAN]标准IEEE 802,11的RADAR[13]。

接近法则是以一组已知点中最接近的点近似作为定位点。

接近法的例子有使用压力传感器的Smart Floor[14]。

另外，室内感应定位系统可以根据是否需要住户随身携带一种设备来分类。

终端方式，例如Active Bats，不需要直接找到住户位置，但是可以感应到住户随身携带的设备位置，例如红外收发器或者射频识别技术（RFID）标签。

因此，如果住户没有随声携带终端设备，那就不可能找到他。

相反的，非终端方式如Easy Living和Smart Floor则不需要这种设备就能找到住户位置。

然而，人们认为Easy Living 侵犯了住户隐私，Smart Floor则是扩展和维护都比较困难。

本文提出一种使用阵列热释电红外（PIR）传感器实现的基于非终端方式的室内感应定位系统[15] [16]。

红外传感器固定在天花板上，并使相邻的传感器的感应范围有重叠。

当它感应到一名住户时，通过多个红外传感器的综合，能够比较准确的确定住户的位置。

该系统不仅具有非终端方式的特有优点，还避免了侵犯隐私，扩展性不佳和维护困难的问题。

为了证明其有效性，已经在实验平台上通过了各种不同测试环境下的实验性评估。

包括此简介，本文共分为四个部分，第二部分介绍基于红外传感器的室内定位感应系统架构（PILAS）以及定位识别算法。

第三部分介绍了基于红外传感器的住户检测法和在实验测试平台上的不同环境下评估系统的表现。

最后一部分为总结和结论。

II. 基于热释电红外传感器的室内感应定位系统架构
A．智能家居的结构
鉴于智能家居的室内环境，室内感应定位系统必须满足一下条件。

第一，由于需要在各种大小不同的房间里安装大量传感器来感知智能家居中的住户，因此定位感应系统需保持较低的成本。

第二，传感器的安装必须是灵活可变的，因为各个房间的形状结构不同，并且还有各样阻碍传感器正常工作的家电和家具。

第三，要求定位感应系统使用的传感器能够抵御很强的噪声，这是因为智能家居能利用各种无线传输技术，比如无线局域网，射频系统，它们都会产生电磁噪声，并且光或温度的巨大变化也会影响传感器的正常工作。

最后该系统的精度可以，根据房间类型作出最合适的调节。

尽管基于热释电红外传感器的这个系统有诸多的优点，但在众多满足要求的产品中并不能吸引人们更多的关注。

它已应用于感应灯（当它感应到人体移动时使灯自动打开），并且成本低于许多其他种类的感应器。

另外，由于热释电红外传感器感应的是人体发出的9.4～10.4微米波长的红外线，从温度、湿度和电磁噪声来说，这种波长相对周围环境较为明显。

而且，它可以通过调整感应半径来控制定位精度，并容易安装在天花板上，这样就不会受到房间结构和障碍物的影响。

图2显示的是为住户提供基于位置的智能服务的PILAS智能家居框架。

在这个框架下，包括热释电红外传感器、房屋终端、智能家居服务器和家用电器在内的各种设备通过家庭网络系统连接在一起。

每个房间被视为一个单元，并在每个单元的天花板上安装适当数量的传感器，为定位服务提供足够的定位精度。

每个红外传感器周期性的感应住户位置，然后将感应信息通过家庭网络系统传输到房屋终端。

因此，房屋终端通过集合来自同一个单元的传感器信息来确定住户的位置，再将住户位置传输到智能家居服务器，服务器就会控制家用电器为住户提供基于位置的定位服务。

图2 PILAS智能家居框架
在这个框架内，智能家居服务器具有以下功能：
（1）虚拟地图发生器为智能家居提供虚拟地图（生成虚拟地图），并在虚拟地图中标出由房屋终端提供的住户位置信息（标注住户位置）。

然后，它通过连接住户的连续定位点来绘制住户的运动轨迹（追踪住户运动）。

（2）家电控制器通过家庭网络系统发送控制命令给家用电器为住户提供智能服务。

（3）运动模式预测器保存当前的住户运动轨迹、家电的动作和反映居家环境的参数，比如时间、温度、湿度、光照度。

储存足够的信息后，它可能会使家电主动提供满足人们需要的人性化的智能服务。

例如，如果智能家居服务器“知道”住户通常在早上7点醒来，之后要淋浴，它也许就会在那一时间打开灯并播放音乐。

另外，住户的淋浴水温也会被自动记录。

B．定位识别算法
为了确定住户在房间里的位置，要使用一组热释电红外传感器，如图3所示。

在此图中，每个传感器的感应面呈圆形并且相邻的几个传感器有重叠的感应范围。

因此，当住户进入某一感应区域后，系统根据从房间内的所有传感器收集到的感应信息判断他/她是否属于这一感应区。

例如，当一位住户进入B感应区，a，b传感器输出“ON”信号，而c传感器输出“OFF”信号。

收集输出信号后，该算法可以推断出住户属于B感应区。

根据传感器的数量和传感信号“ON”的排列，住户的位置通常有以下几种情况。

首先，如果只有一个传感器输出“ON”信号，那么认为住户处于该传感器感应区域的中心位置。

其次，如果有两个相邻的传感器输出“ON”信号，那么认为住户位于两传感器的连线中心点处。

最后，如果有三个或者更多的传感器输出“ON”信号，则认为住户位于所有这些传感器的面心处。

比如，假设住户位于图中的点1处，只有一个传感器a输出“ON”信号，而当住户位于点2处，传感器a和b都输出“ON”信号。

图3 热释电红外传感器的定位识别算法
这个系统的定位精度定义为假设点和住户之间的最大距离。

例如当住户进入A感应区，住户被假设在点1处。

在此假设中，住户可以代表一个点，热释电红外传感器的感应半径为1米，故定位精确度是1米。

因此，当住户位于A感应区的边缘时最有可能发生判断错误。

另外，当住户位于B感应区时，他被假设为在点2处，定位误差最大时就是实际上住户位于点3处。

在这种情况下，定位误差达1.5米，即在点2和点3之间距离是1.5米。

因此，图3显示的这个系统定位精度被视为1米，这是每个区域定位精度的最大有效值。

传感器的数量和它们感应区域的面积决定了PILAS的定位精度，必须合理安排传感器，以保证系统的精确度。

为了准确判断住户位置，提高系统的精确度，这就需要给定数量的传感器有更大的感应范围。

图4显示的是一些传感器不同排列产生的不同感应范围。

图4（a）和（b）显示的是9个传感器不同排列分别产生的40和21的感应范围。

如果从感应范围来看图4（a）中的排列比图4（b）的好。

但是，按照（a）中传感器的排列，在某些区域将无法感应到住户位置，并且定位精度低于（b）。

图4（c）中显示12个传感器排列成28的感应范围内没有任何盲点。

图4 定位精度取决于热释电红外传感器的排列
（a）40感应范围（b）21感应范围（c） 12个传感器构成的28感应范围
当传感器如图4（c）一样排列在房间边缘，他有时会产生尴尬的结果。

图5就是一个例子。

图5（a）显示的是住户的运动路线。

如果我们通过使用传感器定位或相邻传感器的中点来标记系统假设的住户位置点，那么运动路线就会变为如图5（b）显示的折形路线。

为了缓解这一问题，我们认为处于房间边缘的传感器稍微向内补偿，结果就如图5（c）。

图5 中心点外部传感器的补偿效果
（a）住户的运动路线（b）外部传感器补偿之前（c）外部传感器补偿之后
Ⅲ. PILAS的性能评估
A．使用热释电红外传感器的住户检测法
结合来自同一单元内的所有传感器的输出并判断是否是单一的传感器是“ON”或“OFF”来确定住户位置，这直接影响定位精度。

总的来说，由于“ON/OFF”的值是通过预先设定的阈值和从红外传感器的模拟信号中抽样获得的数字输出信号相比较来确定，故选择一个合适的信号电平作为阈值是很有必要的。

例如，使用非终端方式的Smart Floor，因为当住户站在某一点不动时，他的重量通过压力传感器的输出是一个恒定电压，故比较阈值和传感器的输出值可以准确的确定住户位置。

但是，红外传感器测量的是人体移动产生的红外线信号的变化，它输出的是模拟信号，如图6所示。

也就是说，当住户进入一个红外感应区，住户发出红外辐射逐渐增强，红外传感器输出一个增大的电压信号。

相反的，当住户离开感应区，电压信号变小。

如果住户站在感应区不动，红外辐射不变，输出电压为0。

因此，当住户一直停留在某一感应区内，只用红外传感器的电压或电流阈值是很难判断的。