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Maritime Targets Tracking in Heavy-tailed Clutter With Unknown and Time-varying Density

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  • Control Theory and Applications
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Abstract

In order to solve the problem of maritime targets tracking in heavy-tailed sea clutter with unknown and time-varying clutter density, a multi-scan clutter sparsity estimator based amplitude-aided probability hypothesis density (MCSE-APHD) method is proposed in this paper. Firstly, the proposed method eliminates the target-originated measurements from multi-scan cumulative measurement set and estimates the spatial distribution density of clutter online. And the estimated clutter density parameter is fed to the tracker. Secondly, the amplitude-aided likelihood function as well as the estimated clutter parameter is established to update the Gaussian mixture posterior intensity of the state using the probability hypothesis density algorithm. The simulation results verify the effectiveness of the proposed algorithm.

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Authors and Affiliations

  1. School of Automation, Hangzhou Dianzi University, Hangzhou, 310018, China

    Liwei Shi, Yu Kuang & Miaomiao He

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  1. Liwei Shi
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  2. Yu Kuang
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  3. Miaomiao He
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Correspondence to Liwei Shi.

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Liwei Shi received his B.S. degree in electrical automation from Hangzhou Dianzi University, Hangzhou, China, in 2021. He is currently working toward an M.S. degree with the School of Automation. His research interests include maritime dim target tracking and random finite set.

Yu Kuang received his B.S. degree in automation from Hangzhou Dianzi University, Hangzhou, China, in 2021, where he is currently pursuing an M.S. degree with the School of Automation. His research interests include passive target detection and tracking.

Miaomiao He received her B.S. and M.S. degrees in automation from Hangzhou Dianzi University, Hangzhou, China, in 2019 and 2022, respectively. Her research interests include target detection and tracking.

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Shi, L., Kuang, Y. & He, M. Maritime Targets Tracking in Heavy-tailed Clutter With Unknown and Time-varying Density. Int. J. Control Autom. Syst. 22, 1264–1276 (2024). https://doi.org/10.1007/s12555-022-0638-y

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  • DOI: https://doi.org/10.1007/s12555-022-0638-y

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