Steady-state optimal measurement fusion white noise deconvolution estimators

Authors:
Xiao-Jun Sun;Zi-Li Deng
Affiliations:
Department of Automation, Heilongjiang University, Harbin, China;Department of Automation, Heilongjiang University, Harbin, China
Venue:
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Year:
2009

Citing 5
Cited 1

Kalman filtering with real-time applications

Kalman filtering with real-time applications
Optimal and self-tuning white noise estimators with applications to deconvolution and filtering problems

Automatica (Journal of IFAC)
Brief paper: Optimal Kalman filtering fusion with cross-correlated sensor noises

Automatica (Journal of IFAC)
Information fusion white noise deconvolution estimators for time-varying systems

Signal Processing
Technical Communique: Multi-sensor information fusion white noise filter weighted by scalars based on Kalman predictor

Automatica (Journal of IFAC)

Self-tuning weighted measurement fusion white noise deconvolution estimator and its convergence analysis

Digital Signal Processing

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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, a steady-state measurement fusion system is obtained by the weighted least square (WLS) method. A steady-state optimal weighted measurement fusion white noise deconvolution estimator is presented using the Kalman filtering method. By a new derivation method, it is rigorously proved that the steady-state white noise deconvolution fuser is numerically identical to the centralized steady-state white noise deconvolution fuser, i.e. it has the asymptotically global optimality. It can reduce the computational burden because of the lower dimension of the measurement vector. A simulation example for the Bernoulli-Gaussian input white noise shows the effectiveness of the proposed results.