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Two Measurement Set Partitioning Algorithms for the Extended Target Probability Hypothesis Density Filter

The extended target probability hypothesis density (ET-PHD) filter cannot work well if the density of measurements varies from target to target, which is based on the measurement set partitioning algorithms employing the Mahalanobis distance between measurements. To tackle the problem, two measureme...

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Autores principales: Han, Yulan, Han, Chongzhao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6630985/
https://www.ncbi.nlm.nih.gov/pubmed/31200450
http://dx.doi.org/10.3390/s19122665
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author Han, Yulan
Han, Chongzhao
author_facet Han, Yulan
Han, Chongzhao
author_sort Han, Yulan
collection PubMed
description The extended target probability hypothesis density (ET-PHD) filter cannot work well if the density of measurements varies from target to target, which is based on the measurement set partitioning algorithms employing the Mahalanobis distance between measurements. To tackle the problem, two measurement set partitioning approaches, the shared nearest neighbors similarity partitioning (SNNSP) and SNN density partitioning (SNNDP), are proposed in this paper. In SNNSP, the shared nearest neighbors (SNN) similarity, which incorporates the neighboring measurement information, is introduced to DP instead of the Mahalanobis distance between measurements. Furthermore, the SNNDP is developed by combining the DBSCAN algorithm with the SNN similarity together to enhance the reliability of partitions. Simulation results show that the ET-PHD filters based on the two proposed partitioning algorithms can achieve better tracking performance with less computation than the compared algorithms.
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spelling pubmed-66309852019-08-19 Two Measurement Set Partitioning Algorithms for the Extended Target Probability Hypothesis Density Filter Han, Yulan Han, Chongzhao Sensors (Basel) Article The extended target probability hypothesis density (ET-PHD) filter cannot work well if the density of measurements varies from target to target, which is based on the measurement set partitioning algorithms employing the Mahalanobis distance between measurements. To tackle the problem, two measurement set partitioning approaches, the shared nearest neighbors similarity partitioning (SNNSP) and SNN density partitioning (SNNDP), are proposed in this paper. In SNNSP, the shared nearest neighbors (SNN) similarity, which incorporates the neighboring measurement information, is introduced to DP instead of the Mahalanobis distance between measurements. Furthermore, the SNNDP is developed by combining the DBSCAN algorithm with the SNN similarity together to enhance the reliability of partitions. Simulation results show that the ET-PHD filters based on the two proposed partitioning algorithms can achieve better tracking performance with less computation than the compared algorithms. MDPI 2019-06-13 /pmc/articles/PMC6630985/ /pubmed/31200450 http://dx.doi.org/10.3390/s19122665 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Han, Yulan
Han, Chongzhao
Two Measurement Set Partitioning Algorithms for the Extended Target Probability Hypothesis Density Filter
title Two Measurement Set Partitioning Algorithms for the Extended Target Probability Hypothesis Density Filter
title_full Two Measurement Set Partitioning Algorithms for the Extended Target Probability Hypothesis Density Filter
title_fullStr Two Measurement Set Partitioning Algorithms for the Extended Target Probability Hypothesis Density Filter
title_full_unstemmed Two Measurement Set Partitioning Algorithms for the Extended Target Probability Hypothesis Density Filter
title_short Two Measurement Set Partitioning Algorithms for the Extended Target Probability Hypothesis Density Filter
title_sort two measurement set partitioning algorithms for the extended target probability hypothesis density filter
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6630985/
https://www.ncbi.nlm.nih.gov/pubmed/31200450
http://dx.doi.org/10.3390/s19122665
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