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.

• Periodical: Applied Mechanics and Materials (Volumes 48-49)
• Pages: 1018-1023
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.48-49.1018
• Online since: February 2011
• Authors: Jin Fang Liu, Zi Li Deng
• Keywords: ARMA Signal, Convergence, Identification, Information Fusion, Self-Tuning Smoother

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