Collaborative target tracking in WSNs using the combination of maximum likelihood estimation and Kalman filtering  

作　　者：Xingbo WANG Huanshui ZHANG Minyue FU 

机构地区：[1]School of Control Science and Engineering, Shandong University [2]School of Electrical Engineering and Computer Science, University of Newcastle

出　　处：《控制理论与应用（英文版）》2013年第1期27-34,共8页

基　　金：supported by the National Natural Science Foundation for Distinguished Young Scholars of China (No. 60825304);the National Basic Research Development Program of China (973 Program) (No. 2009cb320600);the Open Project of State Key Laboratory of Industrial Control Technology (ICT1006)

摘　　要：Target tracking using wireless sensor networks requires efficient collaboration among sensors to tradeoff between energy consumption and tracking accuracy. This paper presents a collaborative target tracking approach in wire- less sensor networks using the combination of maximum likelihood estimation and the Kalman filter. The cluster leader converts the received nonlinear distance measurements into linear observation model and approximates the covariance of the converted measurement noise using maximum likelihood estimation, then applies Kalman filter to recursively update the target state estimate using the converted measurements. Finally, a measure based on the Fisher information matrix of maximum likelihood estimation is used by the leader to select the most informative sensors as a new tracking cluster for further tracking. The advantages of the proposed collaborative tracking approach are demonstrated via simulation results.Target tracking using wireless sensor networks requires efficient collaboration among sensors to tradeoff between energy consumption and tracking accuracy. This paper presents a collaborative target tracking approach in wire- less sensor networks using the combination of maximum likelihood estimation and the Kalman filter. The cluster leader converts the received nonlinear distance measurements into linear observation model and approximates the covariance of the converted measurement noise using maximum likelihood estimation, then applies Kalman filter to recursively update the target state estimate using the converted measurements. Finally, a measure based on the Fisher information matrix of maximum likelihood estimation is used by the leader to select the most informative sensors as a new tracking cluster for further tracking. The advantages of the proposed collaborative tracking approach are demonstrated via simulation results.

关 键 词：Target tracking Wireless sensor network Maximum likelihood estimation Kalman filtering Fisher information matrix Sensor selection 

分 类 号：TP212.9[自动化与计算机技术—检测技术与自动化装置] TN929.5[自动化与计算机技术—控制科学与工程]