K-coverage based receiver placement optimisation in passive radar network

In passive radar network (PRN), the receiver placement optimisation which has the ability to improve the coverage performance attracts much attention recently. In this study, the K-coverage performance is considered since the pre-condition of deghosting in a radar network is that the target has to b...

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Main Authors: Rui Xie, Xianrong Wan, Kai Luo, Jianxin Yi, Tao Jiang
Format: Article
Language:English
Published: Wiley 2019-08-01
Series:The Journal of Engineering
Subjects:
Online Access:https://digital-library.theiet.org/content/journals/10.1049/joe.2019.0157
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spelling doaj-c2c941b59d24459b92fb47f2345125b72021-04-02T13:07:37ZengWileyThe Journal of Engineering2051-33052019-08-0110.1049/joe.2019.0157JOE.2019.0157K-coverage based receiver placement optimisation in passive radar networkRui Xie0Xianrong Wan1Kai Luo2Jianxin Yi3Tao Jiang4Huazhong University of Science and Technology, School of Electronic Information and CommunicationWuhan University, School of Electronic InformationHuazhong University of Science and Technology, School of Electronic Information and CommunicationWuhan University, School of Electronic InformationHuazhong University of Science and Technology, School of Electronic Information and CommunicationIn passive radar network (PRN), the receiver placement optimisation which has the ability to improve the coverage performance attracts much attention recently. In this study, the K-coverage performance is considered since the pre-condition of deghosting in a radar network is that the target has to be covered by [inline-formula] bistatic pairs. Thus, a K-coverage-based receiver placement optimisation is formulated, in which a weighted M-centre model and the required radar cross section (RCS) for target detection are involved. Moreover, in order to solve this high dimensional optimisation problem, an iterative method which is based on K-order Voronoi algorithm and neighbourood search is proposed. Finally, the proposed method shows its effectiveness in solving the K-coverage optimisation problem of PRN via simulations. Specially, the probability of K-coverage is up to 99.8% with the optimal placement.https://digital-library.theiet.org/content/journals/10.1049/joe.2019.0157object detectionradar receiverscomputational geometryprobabilitypassive radaroptimisationiterative methodsradar cross-sectionsradar signal processingcoverage performancehigh dimensional optimisation problempassive radar networkK-coverage based receiver placement optimisationK-order Voronoi algorithmtarget detectioniterative method
collection DOAJ
language English
format Article
sources DOAJ
author Rui Xie
Xianrong Wan
Kai Luo
Jianxin Yi
Tao Jiang
spellingShingle Rui Xie
Xianrong Wan
Kai Luo
Jianxin Yi
Tao Jiang
K-coverage based receiver placement optimisation in passive radar network
The Journal of Engineering
object detection
radar receivers
computational geometry
probability
passive radar
optimisation
iterative methods
radar cross-sections
radar signal processing
coverage performance
high dimensional optimisation problem
passive radar network
K-coverage based receiver placement optimisation
K-order Voronoi algorithm
target detection
iterative method
author_facet Rui Xie
Xianrong Wan
Kai Luo
Jianxin Yi
Tao Jiang
author_sort Rui Xie
title K-coverage based receiver placement optimisation in passive radar network
title_short K-coverage based receiver placement optimisation in passive radar network
title_full K-coverage based receiver placement optimisation in passive radar network
title_fullStr K-coverage based receiver placement optimisation in passive radar network
title_full_unstemmed K-coverage based receiver placement optimisation in passive radar network
title_sort k-coverage based receiver placement optimisation in passive radar network
publisher Wiley
series The Journal of Engineering
issn 2051-3305
publishDate 2019-08-01
description In passive radar network (PRN), the receiver placement optimisation which has the ability to improve the coverage performance attracts much attention recently. In this study, the K-coverage performance is considered since the pre-condition of deghosting in a radar network is that the target has to be covered by [inline-formula] bistatic pairs. Thus, a K-coverage-based receiver placement optimisation is formulated, in which a weighted M-centre model and the required radar cross section (RCS) for target detection are involved. Moreover, in order to solve this high dimensional optimisation problem, an iterative method which is based on K-order Voronoi algorithm and neighbourood search is proposed. Finally, the proposed method shows its effectiveness in solving the K-coverage optimisation problem of PRN via simulations. Specially, the probability of K-coverage is up to 99.8% with the optimal placement.
topic object detection
radar receivers
computational geometry
probability
passive radar
optimisation
iterative methods
radar cross-sections
radar signal processing
coverage performance
high dimensional optimisation problem
passive radar network
K-coverage based receiver placement optimisation
K-order Voronoi algorithm
target detection
iterative method
url https://digital-library.theiet.org/content/journals/10.1049/joe.2019.0157
work_keys_str_mv AT ruixie kcoveragebasedreceiverplacementoptimisationinpassiveradarnetwork
AT xianrongwan kcoveragebasedreceiverplacementoptimisationinpassiveradarnetwork
AT kailuo kcoveragebasedreceiverplacementoptimisationinpassiveradarnetwork
AT jianxinyi kcoveragebasedreceiverplacementoptimisationinpassiveradarnetwork
AT taojiang kcoveragebasedreceiverplacementoptimisationinpassiveradarnetwork
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