Robust filtering via semidefinite programming with applications to target tracking

作     者：Li, LJ Luo, ZQ Davidson, TN Wong, KM Bossé, E 

作者机构：McMaster Univ Dept Elect & Comp Engn Hamilton ON L8S 4L7 Canada Def Res Estab Valcartier Decis Support Technol Sect Quebec City PQ G0A 1R0 Canada 

出 版 物：《SIAM JOURNAL ON OPTIMIZATION》 (工业与应用数学会最优化杂志)

年 卷 期：2002年第12卷第3期

页      面：740-755页

核心收录：

学科分类：07[理学] 070104[理学-应用数学] 0701[理学-数学] 

主　　题：robust filtering Kalman filtering semidefinite programming target tracking 

摘      要：In this paper we propose a novel finite-horizon, discrete-time, time-varying filtering method based on the robust semidefinite programming (SDP) technique. The proposed method provides robust performance in the presence of norm-bounded parameter uncertainties in the system model. The robust performance of the proposed method is achieved by minimizing an upper bound on the worst-case variance of the estimation error for all admissible systems. Our method is recursive and computationally efficient. In our simulations, the new method provides superior performance to some of the existing robust filtering approaches. In particular, when applied to the problem of target tracking, the new method has led to a significant improvement in tracking performance. Our work shows that the robust SDP technique and the interior point algorithms can bring substantial benefits to practically important engineering problems.