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.
This paper is concerned with the linear quadratic regulation (LQR) problem for both linear discrete-time systems and linear continuous-time systems with multiple delays in a single input channel.Our solution is given in terms of the solution to a two-dimensional Riccati difference equation for the discrete-time case and a Riccati partial differential equation for the continuous-time case.The conditions for convergence and stability are provided.
This note is concerned with the H-infinity deconvolution filtering problem for linear time-varying discretetime systems described by state space models.The H-infinity deconvolution filter is derived by proposing a new approach in Krein space.With the new approach,it is clearly shown that the central deconvolution filter in an H-infinity setting is the same as the one in an H2 setting associated with one constructed stochastic state-space model.This insight allows us to calculate the complicated H-infinity deconvolution filter in an intuitive and simple way.The deconvolution filter is calculated by performing Riccati equation with the same order as that of the original system.
The paper considers the linear quadratic(LQ) control problem for the It-type stochastic system with input del...
Hongxia Wang 1 , Huanshui Zhang 2 , Xuan Wang 1 1. Shenzhen Graduate School of HIT, Shenzhen University Town, Xili, Shenzhen, 518055, P. R. china2. School of Control Science and Engineering, Shandong University, Jingshi Road 17923, Jinan, 250061, P. R. China
＜正＞This paper is concerned with the dynamic Markov jump filters for continuous-time system with random delays ...
HAN Chunyan~1,FENG Gary~1,ZHANG Huanshui~2 1.Department of Manufacturing Engineering and Engineering Management,City University of Hong Kong,Hong Kong 2.School of Control Science and Engineering,Shandong University,Jinan,P.R.China
＜正＞This paper considers the coarsest quantization control problem.Different from the previous works[4]where th...
WEI Li~1,ZHANG Huanshui~1,FU Minyue~2 1.School of Control Science and Engineering,Shandong University,Jingshi Road,Jinan 17923,P.R.China. 2.School of Electrical Engineering and Computer Science,The University of Newcastle,NSW 2308,Callaghan,Australia
