Event-Triggered Distributed Fusion Estimator for Asynchronous Markov Jump Systems with Correlated Noises and Fading Measurements

• Published in: Sensors (Basel, Switzerland) Vol. 24; no. 2; p. 336
• Main Authors: Zhang, Rui, Lin, Honglei
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.01.2024; MDPI
• Subjects: Accuracy; Algorithms; asynchronous Markov jump system; Computer simulation; Computer-generated environments; correlated noise; Data transmission; distributed fusion; Energy consumption; Euclidean space; event-triggered; fading measurement; Fault diagnosis; Markov analysis; Maximum likelihood method; Measurement; Measuring instruments; Sensors; Signal processing; asynchronous Markov jump system; correlated noise; event-triggered; distributed fusion; fading measurement
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s24020336

In this study, we investigate event-triggered distributed fusion estimation for asynchronous Markov jump systems subject to correlated noises and fading measurements. The measurement noises are interrelated, and they are simultaneously coupled with the system noise. The sensor samples measurements uniformly, and the sampling rates of the sensors are different. First, the asynchronous system is synchronized at state update points; then, the local filter is obtained. Furthermore, a variance-based event-triggered strategy is introduced between the local estimator and the fusion center to decrease the energy consumption of network communication. Then, a distributed fusion estimation algorithm is proposed using a matrix-weighted fusion criterion. Finally, the effectiveness of the algorithm is verified using computer simulations.