A novel algorithm for quantized particle filtering with multiple degrading sensors: degradation estimation and target tracking
journal contributionposted 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.
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 CouncilFind out more...
Shandong Provincial Natural Science Foundation of China under Grant ZR2020MF071
Alexander von Humboldt Foundation of Germany
- Aeronautical, Automotive, Chemical and Materials Engineering
- Aeronautical and Automotive Engineering
Published inIEEE Transactions on Industrial Informatics
Pages5830 - 5838
- AM (Accepted Manuscript)
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