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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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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. |
format | Online Article Text |
id | pubmed-6960802 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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