Robust Adaptive Kalman Filtering For Target Tracking With Unknown Observation Noise

作     者：Yongchen Li, Jianxun Li Department of Automation, Shanghai Jiao Tong University, and Key Laboratory of System Control and Information Processing, Ministry of Education of China, Shanghai 200240 

会议名称：《第24届中国控制与决策会议》

会议日期：2012年

学科分类：08[工学] 080203[工学-机械设计及理论] 0802[工学-机械工程] 

基　　金：jointly supported by National Natural Science Foundation (61175008,60935001) 973Project (2009CB824900) the Space Foundation of Supporting-Technology No.2011-HT-SHJD002 Aeronautical Science Foundation of China(20105557007) 

关 键 词：Kalman filter outlier robustness variance estimation adaptability target tracking 

摘      要：The Kalman filter （KF） is widely used in the field of target tracking. In practical target tracking systems through, the observation noise is often unknown and characterized by heavier tails named outliers. That will affect the performance of target tracking seriously and even lead to filtering divergence. To overcome this problem, a novel robust Kalman filter （RKF） is proposed based on the maximum a posteriori （MAP） estimation to observation outliers. In addition, the adaptive estimate of observation noise variance is also given based on the weighted correlation innovation （WCI） sequences of output of a steady state Kalman filter （SSKF）. Finally, a robust adaptive Kalman filter （RAKF） algorithm is raised by implementing RKF and adaptive estimate of simultaneously. The feasibility of the algorithm is demonstrated by an example of target tracking with simulation.