The Self-Tuning Distributed Information Fusion Kalman Filter for ARMA Signals

Abstract

For the multisensor Autoregressive Moving Average (ARMA) signals with unknown model parameters and noise variances, using the Recursive Instrumental Variable (RIV) algorithm, the correlation method and the Gevers-Wouters algorithm with dead band, the fused estimators of unknown model parameters and noise variances can be obtained. Then substituting them into optimal fusion signal filter weighted by scalars, a self-tuning distributed fusion Kalman filter is presented. Using the dynamic error system analysis (DESA) method, it is rigorously proved that the self-tuning fused Kalman signal filter converges to the optimal fused Kalman signal filter, so that it has asymptotic optimality. A simulation example shows its effectiveness.