Self-tuning measurement fusion white noise deconvolution estimator

Authors:
Xiaojun Sun;Zili Deng
Affiliations:
Department of Automation, Heilongjiang University, Harbin, China;Department of Automation, Heilongjiang University, Harbin, China
Venue:
CCDC'09 Proceedings of the 21st annual international conference on Chinese Control and Decision Conference
Year:
2009

Citing 5
Cited 0

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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 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 deconovlution 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.