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Linear-Time Direct Data Assignment Algorithm for Passive Sensor Measurements

To solve the problem of passive sensor data association in multi-sensor multi-target tracking, a novel linear-time direct data assignment (DDA) algorithm is proposed in this paper. Different from existing methods which solve the data association problem in the measurement domain, the proposed algori...

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Detalles Bibliográficos
Autores principales: He, Chaoxin, Zhang, Min, Wu, Guizhou, Guo, Fucheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6960802/
https://www.ncbi.nlm.nih.gov/pubmed/31817195
http://dx.doi.org/10.3390/s19245347
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author He, Chaoxin
Zhang, Min
Wu, Guizhou
Guo, Fucheng
author_facet He, Chaoxin
Zhang, Min
Wu, Guizhou
Guo, Fucheng
author_sort He, Chaoxin
collection PubMed
description To solve the problem of passive sensor data association in multi-sensor multi-target tracking, a novel linear-time direct data assignment (DDA) algorithm is proposed in this paper. Different from existing methods which solve the data association problem in the measurement domain, the proposed algorithm solves the problem directly in the target state domain. The number and state of candidate targets are preset in the region of interest, which can avoid the problem of combinational explosion. The time complexity of the proposed algorithm is linear with the number of sensors and targets while that of the existing algorithms are exponential. Computer simulations show that the proposed algorithm can achieve almost the same association accuracy as the existing algorithms, but the time consumption can be significantly reduced.
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spelling pubmed-69608022020-01-24 Linear-Time Direct Data Assignment Algorithm for Passive Sensor Measurements He, Chaoxin Zhang, Min Wu, Guizhou Guo, Fucheng Sensors (Basel) Article To solve the problem of passive sensor data association in multi-sensor multi-target tracking, a novel linear-time direct data assignment (DDA) algorithm is proposed in this paper. Different from existing methods which solve the data association problem in the measurement domain, the proposed algorithm solves the problem directly in the target state domain. The number and state of candidate targets are preset in the region of interest, which can avoid the problem of combinational explosion. The time complexity of the proposed algorithm is linear with the number of sensors and targets while that of the existing algorithms are exponential. Computer simulations show that the proposed algorithm can achieve almost the same association accuracy as the existing algorithms, but the time consumption can be significantly reduced. MDPI 2019-12-04 /pmc/articles/PMC6960802/ /pubmed/31817195 http://dx.doi.org/10.3390/s19245347 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
He, Chaoxin
Zhang, Min
Wu, Guizhou
Guo, Fucheng
Linear-Time Direct Data Assignment Algorithm for Passive Sensor Measurements
title Linear-Time Direct Data Assignment Algorithm for Passive Sensor Measurements
title_full Linear-Time Direct Data Assignment Algorithm for Passive Sensor Measurements
title_fullStr Linear-Time Direct Data Assignment Algorithm for Passive Sensor Measurements
title_full_unstemmed Linear-Time Direct Data Assignment Algorithm for Passive Sensor Measurements
title_short Linear-Time Direct Data Assignment Algorithm for Passive Sensor Measurements
title_sort linear-time direct data assignment algorithm for passive sensor measurements
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6960802/
https://www.ncbi.nlm.nih.gov/pubmed/31817195
http://dx.doi.org/10.3390/s19245347
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