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

Abstract:

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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.

Info:

Periodical:

Edited by:

Zhixiang Hou

Pages:

1305-1309

DOI:

10.4028/www.scientific.net/AMM.48-49.1305

Citation:

G. L. Tao and Z. L. Deng, "The Self-Tuning Distributed Information Fusion Kalman Filter for ARMA Signals", Applied Mechanics and Materials, Vols. 48-49, pp. 1305-1309, 2011

Online since:

February 2011

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$35.00

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