Self-tuning white noise deconvolution fuser with asymptotic global optimality

摘要

White noise deconvolution or input white noise estimation has a wide range of applications including oil seismic exploration, communication, signal processing, and state estimation. For the multisensor linear discrete time-invariant stochastic systems with correlated noises and unknown noise statistics, an on-line noise statistics estimator is given by the correlation method. Using the Kalman filtering method, based on the self-tuning Riccati equation, a self-tuning weighted measurement fusion white noise deconvolution estimator is presented. By the dynamic error system analysis (DESA) method, it is proved that the self-tuning white noise deconvolution fuser converges to the steady-state optimal white noise deconvolution fuser in a realization so that it has the asymptotic global optimality. A simulation example for a 3-sensor system with Bernoulli-Gaussian input white noise shows its effectiveness.