Event-Based State Estimation: Optimal Algorithm With Generalized Closed Skew Normal Distribution

He, Lidong; Chen, Jiming; Qi, Yifei

doi:10.1109/tac.2018.2829121

Cited by 35 publications

(14 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…According to (30) and Lemma 1 of Reference 11, there is…”

Section: Lemmamentioning

confidence: 99%

“…The widely used Gaussian-approximation assumption is invalid with the deterministic event-based scheduling. 30 In this way, the deterministic event-triggered mechanism (ETM) is not suitable to the measurement step of UKF. In Reference 31, the event-triggered communication between each node is addressed for linear systems, and the consensus of each node is investigated.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Distributed unscented Kalman filtering for nonlinear systems: A mixed event‐triggered strategy

Sun

Yang

2021

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

In this article, the problem of distributed state estimation is addressed for nonlinear systems based on the unscented Kalman filter (UKF) framework. The local estimates are obtained from distributed UKFs with stochastic event-triggered schedules. The consensus of estimated states is achieved by employing the covariance intersection fusion method, while a novel event-triggered strategy for the node-to-node communication is developed. Furthermore, the boundedness and convergence of the covariance matrix are analyzed. Compared with the existing works on distributed Kalman filtering, a new nonlinear global observability condition is proposed to relax the constraint that the system is necessarily observable by each sensor, and the relationship between the convergence and the event-triggering parameters is revealed. Finally, simulations demonstrate the results in this article.

show abstract

“…According to (30) and Lemma 1 of Reference 11, there is…”

Section: Lemmamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Distributed unscented Kalman filtering for nonlinear systems: A mixed event‐triggered strategy

Sun

Yang

2021

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

show abstract

“…Remark 2 In this paper, two alternative conditions are provided to ensure the stability of the designed algorithm (3)-( 7 (8) being satisfied. Particularly, if there is no coupling between nodes, these conditions reduce to the one that (C, A) is detectable.…”

Section: Distributed State Estimationmentioning

confidence: 99%

“…The above problem of sensor scheduling for CPSs has attracted considerable attention from the research communities over the past two decades. According to available information and designing criteria, sensor scheduling can be divided into three typical classes, namely time-based scheduling [17,19], event-based scheduling [6,8,27,29], and performance-based scheduling [12,15,22,30]. First, as an offline strategy, the time-based scheduling policy is easy to design and implement.…”

Section: Introductionmentioning

confidence: 99%

“…In event-based scheduling, the sensor transmission is scheduled by events generated from a triggering condition, which is designed based on state or output variables. Generally, event-based policies can be classified into deterministic schedulers [8,27] and stochastic ones [6,29]. However, event-based policies are usually not optimal for a given performance index.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Sensor Scheduling Design for Complex Networks under a Distributed State Estimation Framework

Duan¹,

He²,

Huang³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

This paper investigates sensor scheduling for state estimation of complex networks over shared transmission channels. For a complex network of dynamical systems, referred to as nodes, a sensor network is adopted to measure and estimate the system states in a distributed way, where a sensor is used to measure a node. The estimates are transmitted from sensors to the associated nodes, in the presence of one-step time delay and subject to packet loss. Due to limited transmission capability, only a portion of sensors are allowed to send information at each time step. The goal of this paper is to seek an optimal sensor scheduling policy minimizing the overall estimation errors. Under a distributed state estimation framework, this problem is reformulated as a Markov decision process, where the one-stage reward for each node is strongly coupled. The feasibility of the problem reformulation is ensured. In addition, an easy-to-check condition is established to guarantee the existence of an optimal deterministic and stationary policy. Moreover, it is found that the optimal policies have a threshold, which can be used to reduce the computational complexity in obtaining these policies. Finally, the effectiveness of the theoretical results is illustrated by several simulation examples.

show abstract

Event-Triggered Distributed Multi-sensor Data Fusion for Networked Systems with Delayed Measurements

Zang

Zhu

Wang

et al. 2022

Proceedings of 2021 5th Chinese Conference on Swarm Intelligence and Cooperative Control

View full text Add to dashboard Cite

Event-Based State Estimation: Optimal Algorithm With Generalized Closed Skew Normal Distribution

Cited by 35 publications

References 18 publications

Distributed unscented Kalman filtering for nonlinear systems: A mixed event‐triggered strategy

Distributed unscented Kalman filtering for nonlinear systems: A mixed event‐triggered strategy

Sensor Scheduling Design for Complex Networks under a Distributed State Estimation Framework

Event-Triggered Distributed Multi-sensor Data Fusion for Networked Systems with Delayed Measurements

Contact Info

Product

Resources

About