Event Based State Estimation With Time Synchronous Updates

Sijs, Joris; Lazăr, Mircea

doi:10.1109/tac.2012.2190197

Cited by 163 publications

(97 citation statements)

References 18 publications

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“…Molin and Hirche [10] investigate the optimal design for sampling in a scalar system with a communication cost by considering a two-player problem. Moreover, Sijs and Lazar [11] study event-driven sampling for the estimation problem with an asymptotic bound on the estimation error covariance.…”

Section: Introductionmentioning

confidence: 99%

Optimal self-driven sampling for estimation based on value of information

Soleymani

Hirche

Baras

2016

2016 13th International Workshop on Discrete Event Systems (WODES)

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Abstract-Consider an observer (reporter) who desires to inform optimally a distant agent regarding a physical stochastic process in the environment while the directed communication of the observer to the agent has a price. We define a metric, from a task oriented perspective, for the information transferred from the observer to the agent. We develop a framework for optimizing an augmented cost function which is a convex combination of the transferred information and the paid price over a finite horizon. We suppose that the decision making takes place inside a source encoder, and that the sampling schedule is the decision variable. Moreover, we assume that no measurement at the current time is available to the observer for the decision making. We derive the optimal self-driven sampling policy using dynamic programming, and we show that this policy corresponds to a self-driven sampling policy based on a quantity that is in fact the value of information at each time instant. In addition, we use a semi-definite programming relaxation to provide a suboptimal sampling policy. Numerical and simulation results are presented for a simple unstable system.

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Section: Introductionmentioning

confidence: 99%

Optimal self-driven sampling for estimation based on value of information

Soleymani

Hirche

Baras

2016

2016 13th International Workshop on Discrete Event Systems (WODES)

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“…Though being widely used in practice, the Gaussianity assumption prohibits to state general properties of the event-triggered state estimator, such as consistency, which also complicates the analysis of its asymptotic behavior. More sophisticated approximations of the posterior distribution incorporating the event information have been conducted in [9] by using a Gaussian sum filter and in [14] by implementing a particle filter. For calculating the exact posterior probability distribution, the work in [12] has shown that the incorporation of event information is closely related to a virtual measurement channel.…”

Section: Introductionmentioning

confidence: 99%

Consistency-preserving event-triggered estimation in sensor networks

Molin

Johansson

2015

2015 54th IEEE Conference on Decision and Control (CDC)

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Abstract-This paper is concerned with linear state estimation in sensor networks with an event-triggered exchange of information. It is assumed that each sensor node transmits its local estimate to a fusion center whenever an appropriately chosen error norm exceeds a threshold. The fusion rule is a modified version of the Covariance Intersection Algorithm. We investigate how to incorporate the event information of not having transmitted at the fusion center such that the filter remains unbiased and consistent with regard to its error covariance. An upper bound on the error covariance matrix is derived by exploiting the structure of the posterior probability distribution. This enables us to replace the event information by the virtual transmission of consistent local estimates. Based on the consistency-preserving property of the proposed scheme, we show stability of the event-triggered state estimator in terms of a bounded mean square error.

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“…The previous work (Sijs and Lazar, 2011;Wu et al, 2013;You and Xie, 2013) gave some approximating algorithms for event-based estimator under the assumption that the predicted estimate or the innovation is Gaussian. Sijs and Lazar (2011) considered the state estimation under general event based sampling, and provided a stable approximation algorithm by using Gaussian sums.…”

Section: Introductionmentioning

confidence: 99%

“…Sijs and Lazar (2011) considered the state estimation under general event based sampling, and provided a stable approximation algorithm by using Gaussian sums. Wu et al (2013), and You and Xie (2013) devised scheduling schemes that the output is communicated based on measurement innovation, and provided recursive algorithms for the discretetime state estimation with the assumption of Gaussian prediction.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Periodic and Event-Based Sampling for Linear State Estimation

Wang

2014

IFAC Proceedings Volumes

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Event Based State Estimation With Time Synchronous Updates

Cited by 163 publications

References 18 publications

Optimal self-driven sampling for estimation based on value of information

Optimal self-driven sampling for estimation based on value of information

Consistency-preserving event-triggered estimation in sensor networks

Comparison of Periodic and Event-Based Sampling for Linear State Estimation

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