Globally Optimal Weighted Fusion White Noise Deconvolution Estimator

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. A globally optimal weighted fusion white noise deconvolution estimator is presented for the multisensor linear discrete systems using the Kalman filtering method. It is derived from the centralized fusion white noise deconvolution estimator so that it is identical to the centralized fuser, i.e. it has the global optimality. Compared with the existing globally suboptimal distributed fusion white noise estimators, the proposed white noise fuser is given based on the local Kalman predictors, and the computation of complex covariance matrices is avoided. A simulation for the Bernoulli-Gaussian input white noise shows the effectiveness of the proposed results.

• Periodical: Advanced Materials Research (Volume 823)
• Pages: 422-427
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.823.422
• Online since: October 2013
• Authors: Xiao Jun Sun
• Keywords: Global Optimality, Kalman Filtering, Multisensor Information Fusion, Weighted Fusion, White Noise Deconvolution

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