Self-tuning distributed measurement fusion Kalman estimator for the multi-channel ARMA signal

For the multisensor multi-channel autoregressive moving average (ARMA) signal with white measurement noises and a common disturbance measurement white noise, when the model parameters and the noise variances are all unknown, a multi-stage information fusion identification method is presented, where...

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Vydáno v:	Signal processing Ročník 91; číslo 8; s. 2028 - 2041
Hlavní autoři:	Ran, Chenjian, Deng, Zili
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Amsterdam Elsevier B.V 01.08.2011 Elsevier
Témata:	Algorithms Applied sciences Asymptotical global optimality Computation Convergence Detection, estimation, filtering, equalization, prediction Distributed measurement fusion Estimates Estimators Exact sciences and technology Fast inversion algorithm Information theory Information, signal and communications theory Inversions Mathematical models Matrices Multi-channel ARMA signal Multisensor information fusion Noise Self-tuning fused signal estimator Signal and communications theory Signal, noise Telecommunications and information theory Fast inversion algorithm Asymptotical global optimality Distributed measurement fusion Self-tuning fused signal estimator Multisensor information fusion Multi-channel ARMA signal Convergence Performance evaluation ARMA model Information integration Error estimation Sensor fusion Dynamical system Correlation method White noise Fast algorithm Multistage method Multisensor Recursive algorithm Computational complexity Inverse problem Simulation Matrix inversion Information processing Recursive method Signal processing Data fusion
ISSN:	0165-1684, 1872-7557
On-line přístup:	Získat plný text
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