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Multichannel ARMA Signal Information Fusion Wiener Filter |
Deng ZiLi;Gao Yuan |
Department of Automation,Heilongjiang University, Harbin 150080,China |
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Abstract Using the Kalman filtering method, based on white noise estimation theory, under the linear minimum variance information fusion criterion, two-sensor information fusion steady-state optimal Wiener filter, smoother and predictor are presented for the multichannel Auto-Regressive Moving Average(ARMA) signals, where the optimal weighting matrices and minimum fused error variance matrix are given. Compared with the single sensor case, the accuracy of the filter is improved. A simulation example of a radar tracking system shows its effectiveness.
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Received: 12 February 2004
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