一种基于无线传感器网络的分布式处理目标跟踪系统

系统使用超声波传感器和扩展卡尔曼滤波对无线传感器网络中的移动单目标进行定位跟踪.节点嵌入式应用程序采用TinyOS/nesC1]编程实现,采用Labview进行应用层开发.为了优化网络的能耗以延长网络寿命,提出了两种在分布式传感器网络中局部节点自适应选择任务节点2]的方法.实验结果验证了扩展卡尔曼算法的正确性,并比较了这两种任务节点选择调度方法的跟踪性能,得出了基于候选节点协方差矩阵最小迹的任务节点选择调度方式在目标丢失率和跟踪精度综合考虑的基础上性能更优.

Distributed processing target tracking system based on wireless sensor networks

This paper discusses the development of a wireless sensor network based target tracking platform. The platform involves ultrasound sensors for target detection and extended Kalman filter (EKF) for tracking a single target. The embedded application is implemented by TinyOS/nesC, and Labview is used to develop the GUI. In order to optimize the energy consumption and to prolong the lifetime of networks, two schemes of local tasking node selection in a self-adaptive way are given in the decentralized sensor networks, including the scheme of nearest distance between target and nodes of candidate and the least trace of covariance matrix scheme. Person as mobile target is used to test the tracking performance. The experiment results validate the correctness of the Extended Kalman Filtering arithmetic and draw a conclusion that the least trace of covariance matrix scheme has better performance when considering the target loss rate and the tracking precision.