The Self-Tuning Distributed Information Fusion Kalman Filter for ARMA Signals

Abstract:

For the multisensor Autoregressive Moving Average (ARMA) signals with unknown model parameters and noise variances, using the Recursive Instrumental Variable (RIV) algorithm, the correlation method and the Gevers-Wouters algorithm with dead band, the fused estimators of unknown model parameters and noise variances can be obtained. Then substituting them into optimal fusion signal filter weighted by scalars, a self-tuning distributed fusion Kalman filter is presented. Using the dynamic error system analysis (DESA) method, it is rigorously proved that the self-tuning fused Kalman signal filter converges to the optimal fused Kalman signal filter, so that it has asymptotic optimality. A simulation example shows its effectiveness.

• Periodical: Applied Mechanics and Materials (Volumes 48-49)
• Pages: 1305-1309
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.48-49.1305
• Online since: February 2011
• Authors: Gui Li Tao, Zi Li Deng
• Keywords: Convergence Analysis, Identification, Information Fusion, Self-Tuning Kalman Filter

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