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A novel algorithm for quantized particle filtering with multiple degrading sensors: degradation estimation and target tracking

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posted on 2022-06-06, 13:15 authored by Yang Liu, Zidong Wang, Cunjia LiuCunjia Liu, Matthew CoombesMatthew Coombes, Wen-Hua ChenWen-Hua Chen
This paper addresses the particle filtering problem for a class of nonlinear/non-Gaussian systems with quantized measurements and multiple degrading sensors. The measurement output of each sensor is quantized by a uniform quantizer before being sent to the remote filter. An augmented system is constructed which aggregates the original system state and the degradation variables. In the presence of the sensor degradation and the quantization errors, a new likelihood function at the remote filter is calculated by resorting to all the transmitted measurements. According to the mathematical characterization of the likelihood function, a novel particle filtering algorithm is developed where the parameters of both the degradation processes and the quantization functions are exploited to obtain the modified importance weights. Finally, the effectiveness of the proposed method is shown via a target tracking example with bearing measurements.

Funding

National Natural Science Foundation of China under Grants 61933007 and 61873148

Goal-Oriented Control Systems (GOCS): Disturbance, Uncertainty and Constraints

Engineering and Physical Sciences Research Council

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Shandong Provincial Natural Science Foundation of China under Grant ZR2020MF071

Alexander von Humboldt Foundation of Germany

History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering

Department

  • Aeronautical and Automotive Engineering

Published in

IEEE Transactions on Industrial Informatics

Volume

19

Issue

4

Pages

5830 - 5838

Publisher

IEEE

Version

  • AM (Accepted Manuscript)

Rights holder

© IEEE

Publisher statement

© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Acceptance date

2022-05-16

Publication date

2022-05-23

Copyright date

2022

ISSN

1551-3203

eISSN

1941-0050

Language

  • en

Depositor

Dr Yang Liu. Deposit date: 4 June 2022

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