For the multisensor multi-channel autoregressive moving average (ARMA) signals with unknown parameters and noisevariances, using the modern time series analysis method, based on the on-line identification of the loca...
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ISBN:
(纸本)9781424421138
For the multisensor multi-channel autoregressive moving average (ARMA) signals with unknown parameters and noisevariances, using the modern time series analysis method, based on the on-line identification of the local ARMA innovation models and fused moving average (MA) innovation model, a class of self-tuning weighted measurement fusion filter and smoother are presented. By using the dynamic error system analysis (DESA) method, it is rigorously proved that the self-tuning signal fusers converge to the optimal signal fusers in a realization. They can reduce the computational burden, and have asymptotic global optimality. A simulation example shows its effectiveness.
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