Self-adjusting Information Fusion Wiener Filter for the Multisensor Signal Systems with Correlated Noise

摘要

With the development of information fusion and filtering theory, we can find a lot of research achievements on target tracking and signal processing. For autoregressive (AR) signal system with multisensor and correlated white noises, if its noise variance and model parameter are unknown or not given, a self-adjusting fused Wiener filter is proposed. The solution to the problem is to substitute estimated values of the unkown elements for real values. The next thing to do is to prove the convergence performance in realization of the self-adjusting fuser obtained above. The DESA (dynamic error system analysis) is made to prove the asymptotic optimality of the self-adjusting fused Wiener filter and checked by a simulation example.