Multi-Sensor State Estimation With a Sequential Stochastic Event-Triggered Mechanism

IF 3 3区计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Transactions on Signal and Information Processing over Networks Pub Date : 2025-03-10 DOI:10.1109/TSIPN.2025.3546477

Zhongyao Hu;Bo Chen;Rusheng Wang;Li Yu

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引用次数: 0

Abstract

This paper investigates the problem of event-triggered (ET) state estimation for multi-sensor systems. An intuitive example is utilized to demonstrate that processing each component of a vector separately can enrich implicit non-triggered information. Inspired by this, a sequential stochastic ET mechanism is proposed, which processes the measurement components one after the other. Particularly, to prevent the failure of the ET mechanism, observability decomposition is performed for each single-sensor subsystem. Then, the Bayes' theorem is utilized to derive the analytic form of the minimum mean-square error estimate. Moreover, the multi-sensor system being collectively detectable is proved to be a sufficient and necessary stability condition for the proposed method. Based on the stability result, we also analyze the relationship between the ET parameter and the communication rate, and provide a design scheme for the optimal ET parameter. Finally, the effectiveness and advantages of the proposed method are verified by a target tracking system.

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利用顺序随机事件触发机制进行多传感器状态估计

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来源期刊

IEEE Transactions on Signal and Information Processing over Networks Computer Science-Computer Networks and Communications

CiteScore

5.80

自引率

12.50%

发文量

期刊介绍： The IEEE Transactions on Signal and Information Processing over Networks publishes high-quality papers that extend the classical notions of processing of signals defined over vector spaces (e.g. time and space) to processing of signals and information (data) defined over networks, potentially dynamically varying. In signal processing over networks, the topology of the network may define structural relationships in the data, or may constrain processing of the data. Topics include distributed algorithms for filtering, detection, estimation, adaptation and learning, model selection, data fusion, and diffusion or evolution of information over such networks, and applications of distributed signal processing.