Information fusion steady-state white noise deconvolution estimators
Sun Xiaojun, Wang Shigang, Deng Zili · 2008
White noise deconvolution or input white noise estimation problem has important application backgrounds in oil seismic exploration, communication and signal processing. Using the modern time series analysis method, based on the autoregressive moving average (ARMA) innovation model and the optimal fusion rules in linear minimum variance sense, the new information fusion white noise deconvolution estimators are presented for the general 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 systems with colored measurement noises. It is locally optimal and globally suboptimal. The accuracy of the fuser is higher than that of each local white noise estimator. 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 Bernoulli-Gaussian input white noise shows their effectiveness and performances.