Optimal filtering for systems with finite-step autocorrelated process noises, random one-step sensor delay and missing measurements

Chen, Dongyan; Xu, Long; Du, Junhua

doi:10.1016/j.cnsns.2015.08.015

Cited by 47 publications

(57 citation statements)

References 38 publications

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“…However, the missing measurements are not considered in the work of Ma and Sun . For uncertain networked systems with multiplicative noises, random one‐step sensor delay, missing measurements, and finite‐step auto‐correlated process noises, based on the minimum mean square error principle, a new linear optimal filter was proposed . However, the packet dropouts are not considered in the work of Chen et al, and it is limited to the single‐sensor systems.…”

Section: Introductionmentioning

confidence: 99%

Robust fusion time‐varying Kalman estimators for multisensor networked systems with mixed uncertainties

Liu

Wang

Deng

2018

Intl J Robust & Nonlinear

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Summary This paper addresses the problem of designing robust fusion time‐varying Kalman estimators for a class of multisensor networked systems with mixed uncertainties including multiplicative noises, missing measurements, packet dropouts, and uncertain‐variance linearly correlated measurement and process white noises. By the augmented approach, the original system is converted into a stochastic parameter system with uncertain noise variances. Furthermore, applying the fictitious noise approach, the original system is converted into one with constant parameters and uncertain noise variances. According to the minimax robust estimation principle, based on the worst‐case system with the conservative upper bounds of the noise variances, the five robust fusion time‐varying Kalman estimators (predictor, filter, and smoother) are presented by using a unified design approach that the robust filter and smoother are designed based on the robust Kalman predictor, which include three robust weighted state fusion estimators with matrix weights, diagonal matrix weights, and scalar weights, a modified robust covariance intersection fusion estimator, and robust centralized fusion estimator. Their robustness is proved by using a combination method, which consists of Lyapunov equation approach, augmented noise approach, and decomposition approach of nonnegative definite matrix, such that their actual estimation error variances are guaranteed to have the corresponding minimal upper bounds for all admissible uncertainties. The accuracy relations among the robust local and fused time‐varying Kalman estimators are proved. A simulation example is shown with application to the continuous stirred tank reactor system to show the effectiveness and correctness of the proposed results.

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Section: Introductionmentioning

confidence: 99%

Robust fusion time‐varying Kalman estimators for multisensor networked systems with mixed uncertainties

Liu

Wang

Deng

2018

Intl J Robust & Nonlinear

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“…Note that most of the relevant results have been concerned with the deterministic delays only, while the communication delays induced by network transmissions would be random and time-varying. As such, the random communication delays have received some initial research interests and the problems of estimation, filtering and fusion have been studied for networked systems with random communication delays [39], [90]- [93]. For example, the filtering problems have been studied in [39], [90], [92], [93] for networked systems with random communication delays modeled by Bernoulli random variables.…”

Section: Communication Delaysmentioning

confidence: 99%

“…As such, the random communication delays have received some initial research interests and the problems of estimation, filtering and fusion have been studied for networked systems with random communication delays [39], [90]- [93]. For example, the filtering problems have been studied in [39], [90], [92], [93] for networked systems with random communication delays modeled by Bernoulli random variables. In [91], the optimal filtering problem has been investigated for networked systems with random communication delays modeled by Markov chain.…”

Section: Communication Delaysmentioning

confidence: 99%

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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“…Hence, the problems of correlated noises and missing measurements attracted the attention of many scholars and a large number of results were published in [8][9][10][11][12][13]. Recently, in [14], the problem of state estimation was solved for a class of linear discrete-time stochastic systems subject to missing data and correlated noises, where the estimators were unbiased and the estimation error covariances were minimized.…”

Section: Introductionmentioning

confidence: 99%

Kalman Filtering Algorithm for Systems with Stochastic Nonlinearity Functions, Finite-Step Correlated Noises, and Missing Measurements

You

Jiang

Huang

et al. 2018

Discrete Dynamics in Nature and Society

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The locally optimal filter is designed for a class of discrete-time systems subject to stochastic nonlinearity functions, finite-step correlated noises, and missing measurements. The multiplicative noises are employed to describe the random disturbances in the system model. The phenomena of missing measurements occur in a random way and the missing probability is characterized by Bernoulli distributed random variables with known conditional probabilities. Based on the projection theory, a class of Kalman-type locally optimal filter is constructed and the filtering error covariance matrix is minimized in the sense of minimum mean square error principle. Also, by solving the recursive matrix equation, we can obtain the filter gain. Finally, two examples are provided: one is a numerical example to illustrate the feasibility and effectiveness of the proposed filtering scheme; the other is to solve the problem of target estimation for a tracking system considering networked phenomena.

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Optimal filtering for systems with finite-step autocorrelated process noises, random one-step sensor delay and missing measurements

Cited by 47 publications

References 38 publications

Robust fusion time‐varying Kalman estimators for multisensor networked systems with mixed uncertainties

Robust fusion time‐varying Kalman estimators for multisensor networked systems with mixed uncertainties

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

Kalman Filtering Algorithm for Systems with Stochastic Nonlinearity Functions, Finite-Step Correlated Noises, and Missing Measurements

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