Self-tuning measurement fusion filter and its convergence analysis
Yuan Gao, Zili Deng · 2008
For the multisensor multi-channel autoregressive moving average (ARMA) signals with unknown parameters and noise variances, using the modern time series analysis method, based on the on-line identification of the local ARMA innovation models and fused moving average (MA) innovation model, a class of self-tuning weighted measurement fusion filter and smoother are presented. By using the dynamic error system analysis (DESA) method, it is rigorously proved that the self-tuning signal fusers converge to the optimal signal fusers in a realization. They can reduce the computational burden, and have asymptotic global optimality. A simulation example shows its effectiveness.