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Self-tuning measurement fusion filter for multisensor ARMA signal and its convergence

For the multisensor autoregressive moving average (ARMA) signal systems with measurement noises, when the ARMA model parameters and noise variances are unknown, using recursive instrumental variable(RIV) algorithm, the correlation method and the Gevers-Wouters algorithm with dead band, the local and fused model parameter estimators and the information fusion noise variance estimators are presented. They have strong consistence. Further, a self-tuning weighted measurement fusion signal filter based on a self-tuning Riccati equation is presented. By the dynamic variance error system analysis(DVSEA) method and the dynamic error system analysis (DESA) method, it is rigorously proved that the self-tuning weighted measurement fusion signal filter converges to the optimal weighted measurement fusion signal filter with probability one, so that it has asymptotic global optimality. A simulation example applied to signal processing shows its effectiveness.