标题：Collaborative target tracking in WSNs using the combination of maximum likelihood estimation and Kalman filtering
作者：Wang Xingbo;Zhang Huanshui;Fu Minyue
作者机构：[Wang Xingbo] School of Control Science and Engineering, Shandong University, Ji'nan, Shandong 250061, China.;[Zhang Huanshui] School of Control Scien 更多
通讯作者地址：[Zhang, H] School of Control Science and Engineering, Shandong University, Jinan Shandong, 250061, China;
关键词：Fisher information matrix; Kalman filtering; Maximum likelihood estimation; Sensor selection; Target tracking; Wireless sensor network
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 wireless 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. © 2013 South China University of Technology, Academy of Mathematics and Systems Science, Chinese Academy of Sciences and Springer-Verlag Berlin Heidelberg.