一种新的基于压缩感知的WSN多目标定位方法  

A New Multiple Target Localization Based on Compressed Sensing Theory in WSN

作　　者：吕伟杰[1] 崔婷婷[1] 刘超[1] 安新升 

机构地区：[1]天津大学电气与自动化工程学院,天津300072

出　　处：《系统仿真技术》2015年第1期6-13,共8页System Simulation Technology

基　　金：国家自然科学基金资助项目(61104208);天津市应用基础与前沿技术研究计划(自然科学基金)资助项目(13JCQNJC00800)

摘　　要：考虑无线传感器网络中定位信息的不完备性,将传感器网络监控区域划分成多个小网格,节点与目标随机分布于网格中,以目标位置信息为稀疏向量,提出了一种新的基于压缩感知的多目标定位方法。该方法将传感器节点感知到的目标数测量矩阵表示为压缩感知理论中测量矩阵、稀疏矩阵与稀疏向量的乘积形式,通过稀疏信号的重构算法恢复目标位置稀疏向量,实现多目标定位。考虑到感知矩阵不满足受限等距性条件,对此矩阵进行了正交化处理,使其满足重构算法的要求。通过仿真分析了节点感知半径、待定位目标数、传感器节点数对目标定位性能的影响。仿真结果表明,在定位信息不完备的情况下,上述方法能够满足无线传感器网络的目标定位要求,且该方法不依赖于硬件测距,其计算复杂度和定位精度与基于接受信号强度(RSS)的压缩感知定位算法相当。Considering the incompleteness of localization information in wireless sensor networks, the sensor network monitoring region is divided into a plurality of small grids. Sensors and targets are randomly dropped in the grids. Defining the targets position information as a sparse vector, a new multiple target localization method based on compressed sensing theory is proposed. The number of perceived targets by sensor nodes is expressed as the product of measurement matrix, sparse matrix and sparse vector in compressed sensing theory. Targets are localized with the sparse signal reconstruction. The sensing matrix is orthogonalized to meet the requirements of the reconstruction algorithm. The method performance varing with sensing radius, targets＇ number and sensors＇ number is analyzed through simulation. The simulation results prove that the proposed method can satisfy the requirements of target localization in wireless sensor network in the case of incomplete information. Furthermore, without physical distance measure, the method has algorithm using received the same computation complexity compared with compressed sensing localization signal strength （RSS）.

关 键 词：无线传感器网络 压缩感知理论 多目标定位 稀疏信号 

分 类 号：TP212[自动化与计算机技术—检测技术与自动化装置]