论文信息 - Self-tuning decoupled fusion Kalman filter based on the Riccati equation

Self-tuning decoupled fusion Kalman filter based on the Riccati equation

An online noise variance estimator for multisensor systems with unknown noise variances is proposed by using the correlationmethod. Based on the Riccati equation and optimal fusion rule weighted by scalars for state components, a self-tuning component decoupled information fusion Kalman filter is presented. It is proved that the filter converges to the optimal fusion Kalman filter in a realization by dynamic error system analysis method, so that it has asymptotic optimality. Its effectiveness is demonstrated by simulation for a tracking system with 3 sensors.

[1] C. J. Harris,et al. Comparison of two measurement fusion methods for Kalman-filter-based multisensor data fusion , 2001 .

[2] Lennart Ljung,et al. System Identification: Theory for the User , 1987 .

[3] Tony T. Lee. A direct approach to identify the noise covariances of Kalman filtering , 1980 .

[4] Lennart Ljung,et al. System identification (2nd ed.): theory for the user , 1999 .

[5] Yuan Gao,et al. New approach to information fusion steady-state Kalman filtering , 2005, Autom..

[6] Zi-Li Deng,et al. Self-tuning Information Fusion Kalman Predictor Weighted by Diagonal Matrices and Its Convergence Analysis , 2007 .

[7] T. Apostol. Mathematical Analysis , 1957 .

[8] Z. Deng,et al. Self-tuning Information Fusion Kalman Predictor Weighted by Scalars , 2006, 2006 6th World Congress on Intelligent Control and Automation.