Distributed weighted robust Kalman filter fusion for uncertain systems with autocorrelated and cross-correlated noises

  • Authors:

  • Jianxin Feng;Zidong Wang;Ming Zeng

  • Affiliations:

  • Space Control and Inertial Technology Research Center, Harbin Institute of Technology, Harbin 150001, China;School of Information Sciences and Technology, Donghua University, Shanghai 200051, China and Department of Information Systems and Computing, Brunel University, Uxbridge, Middlesex UB8 3PH, Unite ...;Space Control and Inertial Technology Research Center, Harbin Institute of Technology, Harbin 150001, China

  • Venue:
  • Information Fusion
  • Year:
  • 2013

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Abstract

In this paper, the problem of distributed weighted robust Kalman filter fusion is studied for a class of uncertain systems with autocorrelated and cross-correlated noises. The system under consideration is subject to stochastic uncertainties or multiplicative noises. The process noise is assumed to be one-step autocorrelated. For each subsystem, the measurement noise is one-step autocorrelated, and the process noise and the measurement noise are two-step cross-correlated. An optimal robust Kalman-type recursive filter is first designed for each subsystem. Then, based on the newly obtained optimal robust Kalman-type recursive filter, a distributed weighted robust Kalman filter fusion algorithm is derived for uncertain systems with multiple sensors. The distributed fusion algorithm involves a recursive computation of the filtering error cross-covariance matrix between any two subsystems. Compared with the centralized Kalman filter, the distributed weighted robust Kalman filter developed in this paper has stronger fault-tolerance ability. Simulation results are provided to demonstrate the effectiveness of the proposed approaches.