Kalman filtering with state‐dependent packet losses

Thapliyal, Omanshu; Nandiganahalli, Jayaprakash Suraj; Hwang, Inseok

doi:10.1049/iet-cta.2018.5425

Cited by 5 publications

(10 citation statements)

References 27 publications

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“…It is noted that the HAKF gain K k+1 in (10) will affect the posteriori estimation error e k+1 defined in (12). So, in the next subsection, we present the optimization of K k+1 to minimize the error e k+1 .…”

Section: Measurement Reconstructionmentioning

confidence: 99%

“…Furthermore, the peakcovariance stability of Kalman filters is proven to be the mean-square stability for a random packet dropout [10,11]. These theories extend to the analysis of Kalman filters with complicated systems [12][13][14][15][16]. The design of Kalman filters with state-dependent packet dropout for the hybrid measurement system is investigated in reference [12].…”

Section: Introductionmentioning

confidence: 99%

“…These theories extend to the analysis of Kalman filters with complicated systems [12][13][14][15][16]. The design of Kalman filters with state-dependent packet dropout for the hybrid measurement system is investigated in reference [12]. Reference [13] proposed a specific estimation design with Markov packet dropout for nonuniform sampling network systems.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Estimation-Based and Dropout-Dependent Control Design for Aeroengine Distributed Control System with Packet Dropout

Pan

Huang

et al. 2022

International Journal of Aerospace Engineering

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A novel estimation-based and dropout-dependent control design for distributed control systems of aeroengines with packet dropout is proposed. The packet dropout is described as an independent identically distributed (i.i.d.) Bernoulli process with known probability. The objective of the control system is to effectively and stably impel aeroengines. An inverse system is proposed to reconstruct the missing measurements, based on which, a hybrid adaptive Kalman filter (HAKF) is proposed to provide the estimated states for control design. A new state-feedback control design is developed with the estimated states and the actual states. For guaranteeing the stability of aeroengines, both the stability of HAKF and the new controlled system are analyzed and proved. The new design is applied to an aeroengine, HAKF has superior estimation accuracy which ensures the effectiveness, and the desired stability performance is achieved by controlled system in the presence of packet dropout.

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Section: Measurement Reconstructionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Estimation-Based and Dropout-Dependent Control Design for Aeroengine Distributed Control System with Packet Dropout

Pan

Huang

et al. 2022

International Journal of Aerospace Engineering

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“…Among the design of control and filtering algorithms in the networked environments, a great deal of effort has been made to examine the impacts from the missing measurements or packet dropouts onto the whole system performance [5], [17], [18], [34], [44]. Accordingly, some efficient filtering methods under different criteria have been reported [16], [32], [38], [46].…”

Section: Introductionmentioning

confidence: 99%

A Prediction-Based Approach to Distributed Filtering With Missing Measurements and Communication Delays Through Sensor Networks

Wang

Liu

et al. 2021

IEEE Trans. Syst. Man Cybern, Syst.

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This article addresses the prediction-based distributed filtering problem for a class of time-varying nonlinear stochastic systems with communication delays and missing measurements through the sensor networks. The phenomenon of the missing measurements is depicted by a set of Bernoulli distributed random variables, where each sensor node possesses its own missing probability. The communication delays are taken into account, which commonly occur during the estimation exchanges among the sensor nodes with communication links. A new prediction-based suboptimal distributed filter is designed by taking the missing probabilities and the prediction estimation into account, which has the advantages on the active compensation of the impacts caused by the missing measurements and communication delays. That is, a new compensation filtering method within the time-varying framework is presented based on the predictive estimation and the innovation measurements. A locally minimum upper bound matrix for the estimation error covariance is obtained by properly designing the distributed filter gain Manuscript

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“…To tackle the state estimation problem related to statedependent packet dropouts (SDPDs), some initial efforts have been made in [37] where the packet loss has been described by a state-dependent hybrid measurement model and the optimal estimation has been accomplished by using the orthogonal projection approach. The pioneering results presented in [37] have been obtained based on a proposed optimal estimator in the linear minimum mean-square error (MMSE) sense.…”

mentioning

confidence: 99%

Recursive Bayesian Estimation for Discrete-Time Systems With State-Dependent Packet Dropouts: A Cross-Coupled Method

Liu,

Wang,

Dong

et al. 2024

IEEE Trans. Automat. Contr.

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In this paper, the recursive Bayesian estimation problem is investigated for a class of linear discrete-time systems subject to state-dependent packet dropouts. During the transmission to a remote estimator, the data packets carrying the local measurements might be dropped if the system state is located within certain occlusion region, and this gives rise to a nonstationary dropout process relying on real system states. In this scenario, due to the exponential growth of the computational cost, it is almost impossible to calculate the exact posterior distribution of the system state for the purpose of optimal state estimation. To address this issue, we propose a novel cross-coupled estimation framework consisting of two interactively working estimators, namely, a region-label estimator and a state estimator, where the former is utilized to obtain the optimal estimates of the regionlabel sequence in the maximum a posteriori sense, while the latter is adopted to achieve the optimal estimates of the system states in the minimum mean-square error sense. Moreover, a sufficient condition is obtained to ensure the mean-square boundedness of the resultant estimation error. The effectiveness of the proposed cross-coupled estimation framework is verified by a numerical simulation example.

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Kalman filtering with state‐dependent packet losses

Cited by 5 publications

References 27 publications

Estimation-Based and Dropout-Dependent Control Design for Aeroengine Distributed Control System with Packet Dropout

Estimation-Based and Dropout-Dependent Control Design for Aeroengine Distributed Control System with Packet Dropout

A Prediction-Based Approach to Distributed Filtering With Missing Measurements and Communication Delays Through Sensor Networks

Recursive Bayesian Estimation for Discrete-Time Systems With State-Dependent Packet Dropouts: A Cross-Coupled Method

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