期刊
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
卷 54, 期 9, 页码 1910-1925出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/00207721.2023.2210145
关键词
Sequential fusion; Markov jump systems; heavy-tailed noise; sensor networks; Student's t distribution
This paper studies the sequential fusion estimation problem for Markov jump multi-sensor systems with heavy-tailed noises. By modeling the noises as Student's t distributions, a sequential fusion estimation algorithm is designed using the interacting multiple model method and Bayes' rule. To improve the robustness against measurement outliers caused by measurement heavy-tailed noise, an F-distribution detection strategy is designed to detect and reject the measurement outliers. Simulation results demonstrate that the designed sequential fusion estimation algorithm can effectively fuse the measurements from multiple sensors, and the accuracy of the designed algorithm is superior to the existing interacting multiple model Student's t batch fusion algorithm and single model adaptive Student's t batch fusion algorithm when there exist model switching and disturbances with heavy-tailed property.
We study a sequential fusion estimation problem for Markov jump multi-sensor systems with heavy-tailed noises. By modelling the noises as Student's t distributions, a sequential fusion estimation algorithm is designed by utilising the interacting multiple model method and Bayes' rule. To improve the robustness against measurement outliers caused by measurement heavy-tailed noise, an F-distribution detection strategy is designed to detect and reject the measurement outliers. Simulation results demonstrate that the designed sequential fusion estimation algorithm can effectively fuse the measurements from multiple sensors, and the accuracy of the designed algorithm is superior to the existing interacting multiple model Student's t batch fusion algorithm and single model adaptive Student's t batch fusion algorithm when there exist model switching and disturbances with heavy-tailed property.
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