## 1 Introduction  
Biometric identification technology mainly consists of two kinds of identification techniques based on physical characteristics and behavioral characteristics, respectively. Physical characteristics usually include face, fingerprint, retina, _etc._; and, behavioral characteristics include signatures, voice, gait, and so forth. Since gait recognition has many advantages in the long-distance identification, it has drawn many researchers' attentions and is becoming an emerging biometric identification research topic. Besides, traditional video systems have been applied to many recognition scenes [1,2]. Such systems, however, primarily identify persons through facial features which are greatly restricted by many external factors such as lighting, angle and clothes. Additionally, they usually have high computational overhead and require huge data throughput. In recent years, with the rapid development of the sensor technology, distributed inference and learning technology, human behavioral information can be detected and measured by passive (e.g., thermal or pressure) or active sensors (e.g., ultrasound or laser); and spatially distributed sensors with decent computation and communication capabilities can work together to achieve complex tasks. Therefore, researchers have attempted to combine their merits to develop the distributed sensor networks based on pyroelectric infrared sensors [3,4]. In order to identify human objects, the pyroelectric infrared sensors have many advantages as follows:  
* Reductions both in the number of measurements and in sampling frequency for human motion state estimation. * Reductions in hardware cost, power consumption, privacy, infringement, computational complexity, communication overhead, and networking data throughput. * Reductions in system deployment duration, limitations upon applications or application location restrictions (e.g., long range or crowded scene). * Its performance is independent of illumination and has strong robustness to the color change of background. * Its sensitivity range of angular rate is about 0.1 r/s to 3 r/s [5,6], which can cover the most human walking speeds at around 2-10 m. It can obtain better field of view (FOV) combined with low price Fresnel lens array. Thus, compared with the traditional video systems, distributed wireless pyroelectric sensor networks can provide better spatial coverage and reduce the deployment duration and deployment location restrictions. Now, distributed pyroelectric infrared network equipped with plenty of sensors can collect various information about changes in the users' states and surroundings. In particular, such systems interpret sensor data as a reflection of user behavior, and thus can build a rich model of the user context. Accordingly, such activity knowledge can help systems adapt their services and computational resources to the user context, e.g., controlling light, or adjusting heating, _etc._  
This paper uses the distributed pyroelectric infrared sensors as the source of information collection in specific environment. Referring recent research [7], the system in this paper consists of four sensor modules, data processing node, wireless gateway and a host computer. Here, each sensor module contains a pyroelecitre infrared sensor which is covered by several Fresnel lens and a signal modulation mask. Once a human target moves into the sensing area of the sensor, the infrared radiation of the humantarget can be captured and transformed into electrical signal which can be transmitted to the host computer to be further processed through the wireless gateway after preliminary noise reduction and data compression. However, before using a distributed wireless pyroelectric infrared sensor network in the identification procedure of motion human target, much attention should be paid to the following items:  
* Different Fresnel lens and signal modulation mask can obtain more pyroelectric infrared information of the human target.