Collaborative Target Tracking in WSNs Based on Maximum Likelihood Estimation and Kalman Filter
Target tracking using wireless sensor networks requires ef.cient collaboration among sensors. Existing collaborative tracking approaches based on the extended Kalman.lter, as addressed in many previous papers, suffer from low tracking accuracy. In this paper, we present a new collaborative target tracking approach in wireless sensor networks based on the combination of maximum likelihood estimation and Kalman.ltering. The leader.rstly converts the nonlinear measurements collected from the scheduled sensors into a linear observation model in target state using maximum likelihood estimation-based localization, then applies a standard Kalman.lter to recursively update the current target state and predict the future target location. Lastly, an information measure based on the Fisher information matrix (FIM) is proposed to select the most informative sensors and one of them is designated as the leader for the next time tracking so as to achieve more tracking accuracy under the energy constraint.
WANG Xingbo ZHANG Huanshui JIANG Xiangyuan
School of Control Science and Engineering, Shandong University, Jingshi Road 73, Jinan, 250061, P.R.China
国际会议
The 30th Chinese Control Conference(第三十届中国控制会议)
烟台
英文
1-6
2011-07-01（万方平台首次上网日期，不代表论文的发表时间）