Self-tuning measurement fusion white noise deconvolution estimator

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 measurement noises and unknown noise statistics, an on-line noise statistics estimator is presented by using the correlation method. Based on the self-tuning Riccati equation, a self-tuning weighted measurement fusion white noise deconvolution estimator is presented using the Kalman filtering method. It is proved that the self-tuning fusion white noise deconvolution estimator converges to the optimal fusion steady-state white noise deconvolution estimator in a realization by the dynamic error system analysis (DESA) method, so that it has the asymptotic global optimality. A simulation example for a tracking system with 3 sensors shows its effectiveness.