Optimal and self-tuning weighted measurement fusion Wiener filter for the multisensor multichannel ARMA signals

DOI10.1016/j.sigpro.2008.10.019zbMath1161.94363OpenAlexW2092116905MaRDI QIDQ1016804

Xiao-Jun Sun, Deng, Zili

Publication date: 14 May 2009

Published in: Signal Processing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.sigpro.2008.10.019

zbMATH Keywords

multisensor information fusionweighted measurement fusionasymptotic global optimalityconvergence in a realizationmultichannel ARMA signalsself-tuning Wiener filter

Mathematics Subject Classification ID

Signal theory (characterization, reconstruction, filtering, etc.) (94A12)

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Cites Work

• Unnamed Item
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• Kalman filtering with real-time applications
• Optimal and self-tuning white noise estimators with applications to deconvolution and filtering problems
• Self-tuning decoupled information fusion Wiener state component filters and their convergence
• Introduction to stochastic control theory
• New approach to information fusion steady-state Kalman filtering
• Optimal self-tuning filtering, prediction, and smoothing for discrete multivariable processes
• Optimal linear estimation fusion. I. Unified fusion rules
• A direct approach to identify the noise covariances of Kalman filtering
• A self-tuning filter for fixed-lag smoothing
• Dynamic ship positioning using a self-tuning Kalman filter
• Approaches to adaptive filtering