摘要
For the multi-sensor linear discrete time-invariant stochastic systems with correlated measurement noises and unknown noise statistics,an on-line noise statistics estimator is obtained using the correlation method.Substituting it into the optimal weighted fusion steady-state white noise deconvolution estimator based on the Kalman filtering,a self-tuning weighted measurement fusion white noise deconvolution estimator is presented.By the Dynamic Error System Analysis(DESA) method,it proved that the self-tuning fusion white noise deconvolution estimator converges to the steady-state optimal fusion white noise deconvolution estimator in a realization.Therefore,it has the asymptotically global optimality.A simulation example for the tracking system with 3 sensors and the Bernoulli-Gaussian input white noise shows its effectiveness.
For the multi-sensor linear discrete time-invariant stochastic systems with correlated measurement noises and unknown noise statistics, an on-line noise statistics estimator is obtained using the correlation method. Substituting it into the optimal weighted fusion steady-state white noise deconvolution estimator based on the Kalman filtering, a self-tuning weighted measurement fusion white noise deconvolution estimator is presented. By the Dynamic Error System Analysis （DESA） method, it proved that the self-tuning fusion white noise deconvolution estimator converges to the steady-state optimal fusion white noise deconvolution estimator in a realization. Therefore, it has the asymptotically global optimality. A simulation example for the tracking system with 3 sensors and the Bernoulli-Gaussian input white noise shows its effectiveness.
基金
Supported by National Natural Science Foundation of China (No.60874063)
Key Laboratory of Electronics Engineering,College of Heilongjiang Province (No.DZZD2010-5),and Science and Automatic Control Key Laboratory of Heilongjiang University
