Distributed state estimation in sensor networks with event-triggered communication

Lei, Yan; Zhang, Xiaomei; Zhang, Zhenjuan; Yang, Yongjie

doi:10.1007/s11071-013-1119-x

Cited by 41 publications

(44 citation statements)

References 24 publications

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“…Product reduction algorithm in Ref. [10] is employed to solve the following conditions. Proposition 1: There exist observers Eqs.…”

Section: Observers' Designmentioning

confidence: 99%

“…In this class of schemes, the system is divided into several smaller subsystems, each governed by a different agent, which may or may not share information with the rest. There exists a vast literature that study the distributed estimation for sensor networks in which the dynamics induced by the communication network (time-varying delays and data losses mainly) are taken into account [2][3][4][5][6][7][8][9][10]. Millan et al [6] have studied the distributed state estimation problem for a class of linear time-invariant systems over sensor networks subject to network-induced delays, which are assumed to have taken values in ½0, τ M .…”

Section: Introductionmentioning

confidence: 99%

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Distributed Consensus‐Based Estimation with Random Delays

Dou¹

2017

Proceedings of the 2nd Czech-China Scientific Conference 2016

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In this chapter we investigate the distributed estimation of linear-invariant systems with network-induced delays and packet dropouts. The methodology is based on local Luenberger-like observers combined with consensus strategies. Only neighbors are allowed to communicate, and the random network-induced delays are modeled as Markov chains. Then, the sufficient and necessary conditions for the stochastic stability of the observation error system are established. Furthermore, the design problem is solved via an iterative linear matrix inequality approach. Simulation examples illustrate the effectiveness of the proposed method.

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“…Product reduction algorithm in Ref. [10] is employed to solve the following conditions. Proposition 1: There exist observers Eqs.…”

Section: Observers' Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Distributed Consensus‐Based Estimation with Random Delays

Dou¹

2017

Proceedings of the 2nd Czech-China Scientific Conference 2016

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“…Then, all estimation errors e j resulting from the distributed event-based estimator (10), (11), (13) with synchronous averaging (19) are bounded for any initial conditionsx j (0) and x(0).…”

Section: Stability Of the Estimation Errormentioning

confidence: 99%

“…where (20) is obtained by direct calculation analogous to (18), and (21) follows from (19). That is, ǫ j is periodically reset to 0 and evolves according to (20) in-between resetting instants.…”

Section: Stability Of the Estimation Errormentioning

confidence: 99%

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Stability analysis of distributed event-based state estimation

Trimpe

2014

53rd IEEE Conference on Decision and Control

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An approach for distributed and event-based state estimation that was proposed in previous work [1] is analyzed and extended to practical networked systems in this paper. Multiple sensor-actuator-agents observe a dynamic process, sporadically exchange their measurements over a broadcast network according to an event-based protocol, and estimate the process state from the received data. The event-based approach was shown in [1] to mimic a centralized Luenberger observer up to guaranteed bounds, under the assumption of identical estimates on all agents. This assumption, however, is unrealistic (it is violated by a single packet drop or slight numerical inaccuracy) and removed herein. By means of a simulation example, it is shown that non-identical estimates can actually destabilize the overall system. To achieve stability, the eventbased communication scheme is supplemented by periodic (but infrequent) exchange of the agents' estimates and reset to their joint average. When the local estimates are used for feedback control, the stability guarantee for the estimation problem extends to the event-based control system. 53rd IEEE Conference on Decision and Control

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Event‐triggered distributed Kalman filter with consensus on estimation for state‐saturated systems

Rezaei

Esfanjani

Akbari

et al. 2020

Intl J Robust & Nonlinear

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This study investigates the problem of event-triggered distributed state estimation for discrete-time, nonlinear systems with state saturation. A Kalman-like filter is developed, and consensus is first achieved with respect to the prediction estimation. The accuracy of the computed estimation is then improved via two recursive equations. The filter gains are determined in each sensor node via utilization of only an upper bound for the common error covariance, thereby resulting in a lower computational burden. Finally, the boundedness of the estimation errors is analyzed, and a comparison of the simulation results demonstrates that the proposed filtering method outperforms a recent rival method. K E Y W O R D S distributed filter, event-triggered transmission, sensor networks, state-saturated system 1 INTRODUCTION An important aspect of multisensor systems is to design distributed filters, wherein the states of a dynamic process are cooperatively estimated based on noisy measurements obtained from several sensor nodes. In distributed filtering, information is exchanged between neighboring sensors to ensure that networked filters can arrive at an agreement (consensus) as a whole on the estimated value. Recently, besides investigating different aspects of networked systems, 1,2 the problem of distributed state estimation over sensor networks has received much attention because of its wide applications, including target tracking, 3,4 communication networks, 5 and industrial operations. 6,7 In general, approaches based on consensus strategy in distributed filtering can be divided into three main categories: (1) consensus on estimation, 8,9 in which the sum of weighted differences between the local and neighboring estimations was added to the estimator of each node; (2) consensus on measurement, 10 wherein consensus is obtained on local measurements and innovation covariance. However, it involves high communication costs and does not assure convergence and (3) consensus on information, 11 especially when local information used is uniform and average, such that the stability of the algorithm is provided at all times. It is clear that the challenges posed as a result of limited communication resources and energy supply should be considered while designing estimators for commonly used battery-operated sensors. Therefore, several studies have been published to reduce unnecessary operations for data transmission in multi-sensor systems by using an event-triggered communication protocol. 12-16 For instance, in the work of Song et al, 15 a distributed extended Kalman filtering problem was suggested for discrete-time nonlinear systems with multiple fading measurements, where an event-triggered communication scheme is used in both the sensor-to-estimator and estimator-to-estimator channels. In the paper

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Distributed state estimation in sensor networks with event-triggered communication

Cited by 41 publications

References 24 publications

Distributed Consensus‐Based Estimation with Random Delays

Distributed Consensus‐Based Estimation with Random Delays

Stability analysis of distributed event-based state estimation

Event‐triggered distributed Kalman filter with consensus on estimation for state‐saturated systems

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