Self-tuning weighted measurement fusion white noise deconvolution estimator and its convergence analysis

Authors:
Xiao-Jun Sun;Guang-Ming Yan
Affiliations:
Department of Automation, Heilongjiang University, Harbin 150080, Peoples Republic of China;Department of Automation, Heilongjiang University, Harbin 150080, Peoples Republic of China
Venue:
Digital Signal Processing
Year:
2013

Citing 6
Cited 0

Optimal and self-tuning white noise estimators with applications to deconvolution and filtering problems

Automatica (Journal of IFAC)
System identification (2nd ed.): theory for the user

System identification (2nd ed.): theory for the user
Information fusion white noise deconvolution estimators for time-varying systems

Signal Processing
Self-tuning decoupled information fusion Wiener state component filters and their convergence

Automatica (Journal of IFAC)
Multi-model information fusion Kalman filtering and white noise deconvolution

Information Fusion
Steady-state optimal measurement fusion white noise deconvolution estimators

CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference

Quantified Score

Hi-index	0.00

Visualization

Abstract

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 with unknown ARMA model parameters and noise statistics, the on-line AR model parameter estimator based on the Recursive Instrumental Variable (RIV) algorithm, the on-line MA model parameter estimator based on Gevers-Wouters algorithm and the on-line noise statistic estimator by using the correlation method are presented. Using the Kalman filtering method, a self-tuning weighted measurement fusion white noise deconvolution estimator is presented based on the self-tuning Riccati equation. 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 using the dynamic error system analysis (DESA) method, so that it has the asymptotic global optimality. The simulation example for a 3-sensor system with the Bernoulli-Gaussian input white noise shows its effectiveness.