Optimal sensor scheduling for hybrid estimation
来源期刊：中南大学学报(英文版)2013年第8期
论文作者：LIU Jian-liang(刘建良) SUN Yao(孙尧) YANG Jian(杨建) LIU Wei-yi(刘为夷) CHEN Wei-min(陈卫民)
文章页码：2186 - 2194
Key words：sensor scheduling; hybrid systems; Bayesian decision risk; target tracking
Abstract: A sensor scheduling problem was considered for a class of hybrid systems named as the stochastic linear hybrid system (SLHS). An algorithm was proposed to select one (or a group of) sensor at each time from a set of sensors. Then, a hybrid estimation algorithm was designed to compute the estimates of the continuous and discrete states of the SLHS based on the observations from the selected sensors. As the sensor scheduling algorithm is designed such that the Bayesian decision risk is minimized, the true discrete state can be better identified. Moreover, the continuous state estimation performance of the proposed algorithm is better than that of hybrid estimation algorithms using only predetermined sensors. Finally, the algorithms are validated through an illustrative target tracking example.
LIU Jian-liang(刘建良)1, SUN Yao(孙尧)1, YANG Jian(杨建)1, LIU Wei-yi(刘为夷)2, CHEN Wei-min(陈卫民)1
(1. School of Information Science and Engineering, Central South University, Changsha 410083, China;
2. Qualcomm Inc, Santa Clara 95051, USA)
Abstract:A sensor scheduling problem was considered for a class of hybrid systems named as the stochastic linear hybrid system (SLHS). An algorithm was proposed to select one (or a group of) sensor at each time from a set of sensors. Then, a hybrid estimation algorithm was designed to compute the estimates of the continuous and discrete states of the SLHS based on the observations from the selected sensors. As the sensor scheduling algorithm is designed such that the Bayesian decision risk is minimized, the true discrete state can be better identified. Moreover, the continuous state estimation performance of the proposed algorithm is better than that of hybrid estimation algorithms using only predetermined sensors. Finally, the algorithms are validated through an illustrative target tracking example.
Key words:sensor scheduling; hybrid systems; Bayesian decision risk; target tracking
