Information fusion white noise deconvolution estimators for time-varying systems

Authors:
Xiao-Jun Sun;Yuan Gao;Zi-Li Deng
Affiliations:
Department of Automation, Heilongjiang University, Harbin 150080, People's Republic of China;Department of Automation, Heilongjiang University, Harbin 150080, People's Republic of China;Department of Automation, Heilongjiang University, Harbin 150080, People's Republic of China
Venue:
Signal Processing
Year:
2008

Citing 6
Cited 7

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

Automatica (Journal of IFAC)
Multisensor optimal information fusion input white noise deconvolution estimators

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Technical Communique: Descriptor Wiener state estimators

Automatica (Journal of IFAC)
Technical Communique: Multi-sensor information fusion white noise filter weighted by scalars based on Kalman predictor

Automatica (Journal of IFAC)
New approach to information fusion steady-state Kalman filtering

Automatica (Journal of IFAC)
Multi-sensor optimal information fusion Kalman filter

Automatica (Journal of IFAC)

Information fusion white noise deconvolution estimators for multisensor systems with different local models

CCDC'09 Proceedings of the 21st annual international conference on Chinese Control and Decision Conference
Self-tuning measurement fusion white noise deconvolution estimator

CCDC'09 Proceedings of the 21st annual international conference on Chinese Control and Decision Conference
Steady-state optimal measurement fusion white noise deconvolution estimators

CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Hybrid model based on wavelet support vector machine and modified genetic algorithm penalizing Gaussian noises for power load forecasts

Expert Systems with Applications: An International Journal
Self-tuning weighted measurement fusion Kalman filtering algorithm

Computational Statistics & Data Analysis
An optimal deconvolution smoother for systems with random parametric uncertainty and its application to semi-blind deconvolution

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

Digital Signal Processing

Quantified Score

Hi-index	0.08

Visualization

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. Based on the Kalman filtering method and the optimal information fusion rules in the linear minimum variance sense, three distributed fused white noise deconvolution estimators weighted by matrices, diagonal matrices, and scalars, are presented for the linear discrete time-varying stochastic systems with multisensor and with different local dynamic models, respectively. The accuracy of the fusers is higher than that of each local white noise estimator. They can handle the white noise fused filtering, smoothing and prediction problems, and are applicable to the multisensor systems with colored measurement noises. In order to compute the optimal weights, the new formula of computing the local estimation error cross-covariances is presented, and the steady-state white noise deconvolution fusers are also presented, which can reduce the on-line computational burden. Two Monte Carlo simulation examples for the Bernoulli-Gaussian input white noise show their effectiveness.