Optimal and self‐tuning weighted measurement fusion Kalman filters and their asymptotic global optimality

Gao, Yuan; Ran, Chenjian; Sun, Xiaojun; Deng, Zili

doi:10.1002/acs.1178

Cited by 40 publications

(17 citation statements)

References 22 publications

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“…Self-tuning decoupled fusion Kalman predictor is proposed in [160] and self-tuning weighted measurement Kalman filter is included in [161]. Self-tuning measurement system using the correlation method, can be viewed as the least-squares (LS) fused estimator and found in [285]. Self-tuning distributed (weighed) measurement fusion Kalman filters is shown in [292,293,294].…”

Section: St-based Distributed Fusion Kalman Fil-termentioning

confidence: 99%

Distributed Kalman Filtering

Khalid¹

2014

Networked Filtering and Fusion in Wireless Sensor Networks

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Section: St-based Distributed Fusion Kalman Fil-termentioning

confidence: 99%

Distributed Kalman Filtering

Khalid¹

2014

Networked Filtering and Fusion in Wireless Sensor Networks

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“…Applying the dynamic error system analysis (DESA) method [9], it can rigorously proved that the self-tuning WMF filter converges to the steady-state optimal WMF filter in a realization, so it has asymptotic global optimality [3].…”

Section: Fused Estimates Of Model Parameters and Noise Variancementioning

confidence: 99%

“…The autoregressive moving average (ARMA) signal model can be transformed to the state space model, so the information fusion estimation problem of the ARMA signal can be solved by estimating the state due to the signal can be considered as the partial components of the state [2]. The weighted measurement fusion(WMF) Kalman filtering method [3] is an important distributed fusion method, which has the global optimality. However, when the measurements to the ARMA signal have sensor bias and common disturbance noise, the computational burden of the fused measurement and the fused measurement noise variance is large.…”

Section: Introductionmentioning

confidence: 99%

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Fast self-tuning weighted measurement fusion Kalman filter for the ARMA signal

Ran

Deng

2011

2011 IEEE International Conference on Mechatronics and Automation

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For the multisensor single channel autoregressive moving average(ARMA) signal with common disturbance measurement noise and sensor bias, when the model parameters, the sensor bias and the noise variances are all unknown, their consistent estimates are obtained by a multistage fused identification method, which includes the recursive extended least squares (RELS) algorithm, correlation method and the Gevers-Wouters algorithm with a dead band. Substituting these estimates into the optimal weighted measurement fusion(WMF) Kalman signal filter, a self-tuning WMF Kalman signal filter with asymptotic global optimality is presented. A fast inversion algorithm of the extended Pei-Radman matrix is presented in order to reduce the computational load. A simulation example verifies the effectiveness of the proposed method.Index Terms-Multisensor data fusion, ARMA signal, multistage identification method, Fast inversion algorithm, Selftuning fused signal filter

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“…Based on the weighted least squares (WLS) method, 4 2 optimal weighted measurement fusion (WMF) algorithms have been presented. 5,6 Based on the unbiased linear minimum variance (ULMV) weighting fusion rule, 3 optimal weighted state fusion algorithms have been presented. 7,8 The input white noise estimation problem for stochastic systems is called white noise deconvolution, which has important applications in oil seismic exploration 9 and occurs in many fields, including communications, signal processing, and state estimation.…”

mentioning

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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Optimal and self‐tuning weighted measurement fusion Kalman filters and their asymptotic global optimality

Cited by 40 publications

References 22 publications

Distributed Kalman Filtering

Distributed Kalman Filtering

Fast self-tuning weighted measurement fusion Kalman filter for the ARMA signal

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

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