Multi-model information fusion Kalman filtering and white noise deconvolution

Sun, Xiaojun; Gao, Yuan; Deng, Zili; Li, Chuang; Wang, Jiawei

doi:10.1016/j.inffus.2009.06.004

Cited by 112 publications

(60 citation statements)

References 11 publications

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“…According to Sun et al, 15 the CF and WMF white noise deconvolution estimation errorw ( j) (t|t + N) = w(t) −ŵ ( j) (t|t + N), N ≥ 0 satisfy the following formula:…”

Section: Robust Cf and Wmf Time-varying White Noise Deconvolution Estmentioning

confidence: 99%

Robust centralized and weighted measurement fusion white noise deconvolution estimators for multisensor systems with mixed uncertainties

Liu

Wang

Deng

2017

Adaptive Control & Signal

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SummaryEstimating the input signal of a system is called deconvolution or input estimation. The white noise deconvolution has important applications in oil seismic exploration, communications, and signal processing. This paper addresses the design of robust centralized fusion (CF) and weighted measurement fusion (WMF) white noise deconvolution estimators for a class of uncertain multisensor systems with mixed uncertainties, including uncertain-variance multiplicative noises in measurement matrix, missing measurements, and uncertain-variance linearly correlated measurement and process white noises. By introducing the fictitious noise, the considered system is converted into one with only uncertain noise variances. According to the minimax robust estimation principle, based on the worst-case system with the conservative upper bounds of uncertain noise variances, the robust CF and WMF time-varying white noise deconvolution estimators (predictor, filter, and smoother) are presented in a unified framework. Applying the Lyapunov equation approach, their robustness is proved in the sense that their actual estimation error variances are guaranteed to have the corresponding minimal upper bounds for all admissible uncertainties. Using the information filter, their equivalence is proved. Their accuracy relations are proved. The computational complexities are analyzed and compared. Compared with the CF algorithm, the WMF algorithms can significantly reduce the computational burden when the number of sensors is larger. The corresponding robust fused steady-state white noise deconvolution estimators are also presented. A simulation example with respect to the multisensor IS-136 communication systems shows the effectiveness and correctness of the proposed results.

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“…According to Sun et al, 15 the CF and WMF white noise deconvolution estimation errorw ( j) (t|t + N) = w(t) −ŵ ( j) (t|t + N), N ≥ 0 satisfy the following formula:…”

Section: Robust Cf and Wmf Time-varying White Noise Deconvolution Estmentioning

confidence: 99%

Robust centralized and weighted measurement fusion white noise deconvolution estimators for multisensor systems with mixed uncertainties

Liu

Wang

Deng

2017

Adaptive Control & Signal

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“…Based on conservative WMF steady-state Kalman predictor, for the worst-case WMF systems (1) and (14) Sun et al [15] proved that filtering and smoothing errors…”

Section: A the First Class Of Guaranteed Cost Robust Wmf Kalman Filtmentioning

confidence: 99%

Guaranteed Cost Robust Weighted Measurement Fusion Kalman Estimators With Uncertain Noise Variances and Missing Measurements

Yang

Deng

2016

IEEE Sensors J.

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This paper is concerned with guaranteed cost robust weighted measurement fusion (WMF) estimation problem for multisensor system with both uncertain noise variances and missing measurements. By introducing the fictitious measurement white noises, the original multisensor system is converted into one only with uncertain noise variances. Two classes of guaranteed cost robust WMF Kalman estimators (predictor, filter and smoother) are presented by the Lyapunov equation approach, based on the minimax robust estimation principle and the parameterization representation of uncertain noise variance perturbations. The maximal lower bound and minimal upper bound of actual accuracy deviations are given. A unified approach of designing the robust WMF Kalman estimators is presented based on the robust WMF Kalman predictor. A simulation example shows the correctness and effectiveness of the proposed results.

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“…However, all of the above weighting fusers have the limitation that in order to compute the optimal weights, the computation of the cross-covariances between the local estimation errors is required, while the cross-covariances are usually unknown [6] or their computation is very complex [7] in many applications. In order to overcome this limitation, the covariance intersection fusion method has been presented and developed in [6,8,9] which can solve the fused filtering problems with unknown cross-covariances, and have the consistency and robustness.…”

Section: Introductionmentioning

confidence: 99%

Distributed Weighting Fusion and Covariance Intersection Fusion Kalman Smoother for Systems with Colored Measurement Noises

Zhang¹,

Qi²

2016

Proceedings of the 2016 International Conference on Intelligent Control and Computer Application

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Abstract-For two-sensor system with colored measurement noises, based on the classical Kalman filtering method, distributed weighting fusion method and the covariance intersection (CI) fusion method, a CI fusion steady-state Kalman smoother is presented, which is independent of the unknown cross-covariance. It is difficult or complex to compute the crosscovariance in most cases. The accuracy comparison of the CI fusion Kalman smoother with three classical distributed weighting fusion Kalman smoothers which are weighted respectively by matrices, diagonal matrices, and scalars is proven, i.e., its accuracy is higher than that of each local Kalman smoother, and lower than that of optimal fuser weighted by matrices with known cross-covariance. The formula of the actual smoothing error variance of the CI fuser is given and the geometric interpretation of the above accuracy relations is presented based on the covariance ellipses. The Monte-Carlo simulation results show its effectiveness and correctness.

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Multi-model information fusion Kalman filtering and white noise deconvolution

Cited by 112 publications

References 11 publications

Robust centralized and weighted measurement fusion white noise deconvolution estimators for multisensor systems with mixed uncertainties

Robust centralized and weighted measurement fusion white noise deconvolution estimators for multisensor systems with mixed uncertainties

Guaranteed Cost Robust Weighted Measurement Fusion Kalman Estimators With Uncertain Noise Variances and Missing Measurements

Distributed Weighting Fusion and Covariance Intersection Fusion Kalman Smoother for Systems with Colored Measurement Noises

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