Self-tuning Fusion Kalman Filter with Unknown Parameters and Its Convergence
含未知参数的自校正融合Kalman滤波器及其收敛性

For the multisensor systems with unknown model parameters and noise variances, a self-tuning decoupled fused Kalman filter is presented based on the optimal fusion rule weighted by scalars for components. Its convergence is proved by using the dynamic error system analysis (DESA) method. As an application to signal processing, the multidimensional and multiple bias compensated recursive least-squares (BCRLS) algorithms for estimating the AR parameters are presented for the multisensor multidimensional autoregressive (AR) signal with white and colored measurement noises. The equivalence between the two BCRLS algorithms is proved. The convergence of the two BCRLS algorithms is proved by DESA method. Further more, a self-tuning fused Kalman filter for the AR signal is presented, which has asymptotic optimality. A simulation example shows the effectiveness.