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- W1966343682 abstract "White noise deconvolution or input white noise estimation has a wide range of applications including oil seismic exploration, communication, signal processing, and state estimation. For the multisensor linear discrete time-invariant stochastic systems with correlated measurement noises and unknown noise statistics, an on-line noise statistics estimator is presented by using the correlation method. Based on the self-tuning Riccati equation, a self-tuning weighted measurement fusion white noise deconvolution estimator is presented using the Kalman filtering method. It is proved that the self-tuning fusion white noise deconovlution estimator converges to the optimal fusion steady-state white noise deconvolution estimator in a realization by the dynamic error system analysis (DESA) method, so that it has the asymptotic global optimality. A simulation example for a tracking system with 3 sensors shows its effectiveness." @default.
- W1966343682 created "2016-06-24" @default.
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- W1966343682 date "2009-06-01" @default.
- W1966343682 modified "2023-09-26" @default.
- W1966343682 title "Self-tuning measurement fusion white noise deconvolution estimator" @default.
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- W1966343682 doi "https://doi.org/10.1109/ccdc.2009.5191933" @default.
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