Distributed weighted robust Kalman filter fusion for uncertain systems with autocorrelated and cross-correlated noises

Feng, Jianxin; Wang, Zidong; Zhang, Ming

doi:10.1016/j.inffus.2011.09.004

Cited by 170 publications

(146 citation statements)

References 30 publications

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“…On the other hand, according to the matrix-weighted fusion mechanism, several distributed fusion algorithms have been developed in order to improve the fault-tolerance ability [160]- [163]. To be more specific, the Kalman-like distributed fusion filters (one-step predictors) have been constructed in [160] for linear multi-sensor time-varying stochastic system in the simultaneous presence of parameter uncertainties, missing measurements and unknown measurement disturbances, and the optimal filter gains have been obtained based on the linear unbiased minimum variance criterion.…”

Section: B Distributed Filtering and Fusion For Networked Systems Ovmentioning

confidence: 99%

“…Besides, when there exist the auto-correlated and cross-correlated noises, a robust distributed weighted Kalman filter fusion method has been presented in [163] for a class of uncertain time-varying systems with stochastic uncertainties without resorting the state augmentation method. By using the projection theory, an optimal fusion algorithm has been given in [164] for a class of multi-sensor stochastic singular systems with multiple state delays and measurement delays.…”

Section: B Distributed Filtering and Fusion For Networked Systems Ovmentioning

confidence: 99%

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Estimation, filtering and fusion for networked systems with network-induced phenomena: New progress and prospects

et al. 2016

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In this paper, some recent advances on the estimation, filtering and fusion for networked systems are reviewed. Firstly, the network-induced phenomena under consideration are briefly recalled including missing/fading measurements, signal quantization, sensor saturations, communication delays, and randomly occurring incomplete information. Secondly, the developments of the estimation, filtering and fusion for networked systems from four aspects (linear networked systems, nonlinear networked systems, complex networks and sensor networks) are reviewed comprehensively. Subsequently, some recent results on the estimation, filtering and fusion for systems with the network-induced phenomena are reviewed in great detail. In particular, some latest results on the multiobjective filtering problems for time-varying nonlinear networked systems are summarized. Finally, conclusions are given and several possible research directions concerning the estimation, filtering, and fusion for networked systems are highlighted.

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Section: B Distributed Filtering and Fusion For Networked Systems Ovmentioning

confidence: 99%

Section: B Distributed Filtering and Fusion For Networked Systems Ovmentioning

confidence: 99%

Estimation, filtering and fusion for networked systems with network-induced phenomena: New progress and prospects

et al. 2016

Self Cite

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“…Song et al presents Kalman filtering fusion [11] with 25 feedback and cross-correlated sensor noises for distributed recursive state estimators. The cross-correlation between the measurement noise and process noise are discussed in [12,13,14]. For noise sequences with uncertain variances, the actual filtering error variances [15,16,17] are obtained with a minimal upper bound for all admissible uncertainties.…”

Section: Introductionmentioning

confidence: 99%

Distributed weighted fusion estimation for uncertain networked systems with transmission time-delay and cross-correlated noises

Liu

Yang

et al. 2017

Neurocomputing

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This paper investigates the state estimation issue for uncertain networked systems considering data transmission time-delay and cross-correlated noises. A distributed robust Kalman filtering-based perception and centralized fusion method is proposed to improve the estimation accuracy from perturbed measurement; consequently, reduce the amount of redundant information and alleviate the estimation burden. To describe the transmission time-delay and give rise to cross-correlated and state-dependent noises in the exchange measurement among neighbors, a weighted fusion reorganized innovation strategy is proposed to reduce the computational burden and suppress noise effect. Moreover, to obtain the optimal linear estimate, a fusion estimation approach is used for information collaboration by weighting the error cross-covariance matrices. Finally, an illustrative example is presented to demonstrate the effectiveness and robustness of the proposed method.

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“…So, important advances have been achieved concerning the estimation problem in networked stochastic systems and the design of multisensor fusion techniques [1]. Many of the existing fusion estimation algorithms are related to conventional systems (see e.g., [2][3][4][5], and the references therein), where the sensor measured outputs are affected only by additive noises and each sensor transmits its outputs to the fusion center over perfect connections.…”

Section: Introductionmentioning

confidence: 99%

Fusion Estimation from Multisensor Observations with Multiplicative Noises and Correlated Random Delays in Transmission

2017

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Abstract:In this paper, the information fusion estimation problem is investigated for a class of multisensor linear systems affected by different kinds of stochastic uncertainties, using both the distributed and the centralized fusion methodologies. It is assumed that the measured outputs are perturbed by one-step autocorrelated and cross-correlated additive noises, and also stochastic uncertainties caused by multiplicative noises and randomly missing measurements in the sensor outputs are considered. At each sampling time, every sensor output is sent to a local processor and, due to some kind of transmission failures, one-step correlated random delays may occur. Using only covariance information, without requiring the evolution model of the signal process, a local least-squares (LS) filter based on the measurements received from each sensor is designed by an innovation approach. All these local filters are then fused to generate an optimal distributed fusion filter by a matrix-weighted linear combination, using the LS optimality criterion. Moreover, a recursive algorithm for the centralized fusion filter is also proposed and the accuracy of the proposed estimators, which is measured by the estimation error covariances, is analyzed by a simulation example.

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Distributed weighted robust Kalman filter fusion for uncertain systems with autocorrelated and cross-correlated noises

Cited by 170 publications

References 30 publications

Estimation, filtering and fusion for networked systems with network-induced phenomena: New progress and prospects

Estimation, filtering and fusion for networked systems with network-induced phenomena: New progress and prospects

Distributed weighted fusion estimation for uncertain networked systems with transmission time-delay and cross-correlated noises

Fusion Estimation from Multisensor Observations with Multiplicative Noises and Correlated Random Delays in Transmission

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