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

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• 作者：Xiao-Jun Sun ; ^{sxj@hlju.edu.cn}Author Vitae ; Guang-Ming YanAuthor Vitae
• 关键词：Multisensor information fusion ; Weighted measurement fusion ; Self-tuning fuser ; White noise deconvolution ; Convergence
• 刊名：Digital Signal Processing
• 出版年：2013
• 出版时间：January, 2013
• 年：2013
• 卷：23
• 期：1
• 页码：38-48
• 全文大小：356 K

文摘

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.