Universal Delayed Kalman Filter with Measurement Weighted Summation for the Linear Time Invariant System  

作　　者：GE Quanbo XU Tingliang FENG Xiaoliang WEN Chenglin 

机构地区：[1]State Key Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou 310027, China [2]Institute of Information and Control, Hangzhou Dianzi University, Hangzhou 310018, China [3]College of Computer and Information, Hohai University, Nanjing 310098, China

出　　处：《Chinese Journal of Electronics》2011年第1期67-72,共6页电子学报（英文版）

基　　金：This work is supported by the National Natural Science Foundation of China （No.60934009, No.60804064, No.60572051）, China Postdoctoral Science Foundation （No.YK 2008061）, Project of Science and Technology Department of Zhejiang Province （No.2009C34016）, and Zhejiang Graduate Innovation Project （No.YK.2008061）.

摘　　要：This paper considers the design of universal delayed Kalman filter for the networked tracking system with arbitrary random delay. Firstly, an equivalent Weighted summation form of the conventional Kalman filter （WSFKF） is given to provide a novel frame to more effectively solve the delayed filtering or Out-of-sequence measurements （OOSMs） estimate. In nature, this form makes perfectly use of the properties of offline parameters computation for Kalman filter and weighted summation of initial state estimate and the ordered measurements, which are respectively from Linear time invariant （LTI） system and Linear minimum mean square error （LMMSE） estimator. Secondly, by combing a replacement with global measurement prediction and a compensation operation based on the innovation of delayed measurement and adaptive online weighted coefficient matrix, a novel universal delayed Kalman filter which is applicable to the arbitrary random delay is designed under the WSFKF frame. Compared with the current delayed filters or OOSMs update methods, the proposed delayed estimator has not only more concise algorithm structure and better estimate accuracy but also stronger application range. The example is demonstrated to validate the proposed delayed estimator in this paper.

关 键 词：Kalman filter Linear time invariant system Linear minimum mean square error （LMMSE） Random delay Measurement summation. 

分 类 号：TN713[电子电信—电路与系统] O231[理学—运筹学与控制论]