一种基于PCRLB和粒子滤波的目标跟踪算法  

Target tracking algorithm based on PCRLB and particle filter

作　　者：胡钧[1] 杨震[2] 孙飞[2] 

机构地区：[1]南京邮电大学通信与信息工程学院,江苏南京210003 [2]南京邮电大学宽带无线通信与传感网技术教育部重点实验室,江苏南京210003

出　　处：《南京邮电大学学报（自然科学版）》2015年第5期53-59,共7页Journal of Nanjing University of Posts and Telecommunications：Natural Science Edition

基　　金：国家重点基础研究发展计划(973计划)(2011CB302903);国家自然科学基金(61271335);江苏省高校自然科学研究重大项目(14KJA510003)资助项目

摘　　要：无线传感器网络执行目标跟踪时,各个节点的贡献不一样,因此一种好的节点规划机制可以获得更好的跟踪性能。为了降低传输数据量,通常节点不是将测量到的原始数据直接发送给簇首进行数据融合,而是先进行本地压缩量化。针对簇首使用自己的测量值和普通任务节点量化后的测量值进行融合这一纯方位角跟踪应用场景,提出了一种基于PCRLB(Posterior Cramer-Rao Lower Bound)的节点规划方法,同时设计了相应的粒子滤波算法来估计目标状态。仿真结果表明,文中提出的方案与基于KL(Kullback-Leibler)距离的节点规划算法、簇首和普通任务节点随机选择算法相比,有更好的跟踪性能。The contribution of each sensor is different from each other in wireless sensor network for target tracking,therefore,a superior mechanism of sensor schedule is likely to achieve better tracking performance. In order to reduce data transmission,sensor quantizes and compresses the measurement locally before transmitting it to cluster head to conduct data fusion,rather than send the raw data directly. A method is introduced to schedule sensors based on posterior Cramer-Rao lower bound（ PCRLB） with the raw measurement of cluster head and the quantized measurements of ordinary task sensors in the fusion process in the bearing-only tracking scenario. The corresponding particle filter algorithm is designed to estimate the target state. Simulation results show that the proposed method has better tracking performance compared than the Kullback-Leibler（ KL） divergence-based sensor schedule algorithm and random selection algorithm of cluster head and ordinary task sensors.

关 键 词：无线传感器网络 目标跟踪 节点规划 PCRLB 粒子滤波 

分 类 号：TN926[电子电信—通信与信息系统]