Paper Title:
Self-Tuning Information Fusion Wiener Smoother for ARMA Signals
  Abstract

For the multisensor autoregressive moving average (ARMA) signals, based on the modern time series analysis method, a self-tuning information fusion Wiener smoother is presented when both model parameters and noise variances are unknown. The principle is that substituting the estimators of unknown parameters and noise variances into the corresponding optimal fusion Wiener smoother will yield a self-tuning fuser. Further, applying the dynamic error system analysis (DESA) method, it is rigorously proved that the self-tuning fused Wiener smoother converges to the optimal fused Wiener smoother in a realization, i.e. it has asymptotic optimality. A simulation example shows its effectiveness.

  Info
Periodical
Edited by
Zhixiang Hou
Pages
1018-1023
DOI
10.4028/www.scientific.net/AMM.48-49.1018
Citation
J. F. Liu, Z. L. Deng, "Self-Tuning Information Fusion Wiener Smoother for ARMA Signals", Applied Mechanics and Materials, Vols. 48-49, pp. 1018-1023, 2011
Online since
February 2011
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$32.00
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