Multisensor information fusion steady-state white noise deconvolution estimators

Abstract

White noise deconvolution or input white noise estimation problem has important application backgrounds in oil seismic exploration, communication and signal processing. By the modern time series analysis method, based on the autoregressive moving average (ARMA) innovation model, under the linear minimum variance optimal fusion rules, three optimal weighted fusion white noise deconvolution estimators are presented for the multisensor systems with different local dynamic models and correlated noises, respectively. They can handle the input white noise fused filtering, prediction and smoothing problems, and are applicable for the multisensor systems with colored measurement noises. They are locally optimal and globally suboptimal. The accuracy of the fusers is higher than that of each local white noise estimator, and is lower than that of the centralized fuser. In order to compute the optimal weights, the formula of computing the local estimation error cross-covariances is given. A Monte Carlo simulation example for the system with 3 sensors and the Bernoulli-Gaussian input white noise shows their effectiveness and performances.