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A Barrage Jamming Strategy Based on CRB Maximization against Distributed MIMO Radar
Distributed multiple input multiple output (MIMO) radar has attracted much attention for its improved detection and estimation performance as well as enhanced electronic counter-counter measures (ECCM) ability. To protect the target from being detected and tracked by such radar, we consider a barrag...
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/PMC6603513/ https://www.ncbi.nlm.nih.gov/pubmed/31146361 http://dx.doi.org/10.3390/s19112453 |
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author | Zheng, Guangyong Na, Siqi Huang, Tianyao Wang, Lulu |
author_facet | Zheng, Guangyong Na, Siqi Huang, Tianyao Wang, Lulu |
author_sort | Zheng, Guangyong |
collection | PubMed |
description | Distributed multiple input multiple output (MIMO) radar has attracted much attention for its improved detection and estimation performance as well as enhanced electronic counter-counter measures (ECCM) ability. To protect the target from being detected and tracked by such radar, we consider a barrage jamming strategy towards a distributed MIMO. We first derive the Cramer–Rao bound (CRB) of target parameters estimation using a distributed MIMO under barrage jamming environments. We then set maximizing the CRB as the criterion for jamming resource allocation, aiming at degrading the accuracy of target parameters estimation. Due to the non-convexity of the CRB maximizing problem, particle swarm optimization is used to solve the problem. Simulation results demonstrate the advantages of the proposed strategy over traditional jamming methods. |
format | Online Article Text |
id | pubmed-6603513 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-66035132019-07-19 A Barrage Jamming Strategy Based on CRB Maximization against Distributed MIMO Radar Zheng, Guangyong Na, Siqi Huang, Tianyao Wang, Lulu Sensors (Basel) Article Distributed multiple input multiple output (MIMO) radar has attracted much attention for its improved detection and estimation performance as well as enhanced electronic counter-counter measures (ECCM) ability. To protect the target from being detected and tracked by such radar, we consider a barrage jamming strategy towards a distributed MIMO. We first derive the Cramer–Rao bound (CRB) of target parameters estimation using a distributed MIMO under barrage jamming environments. We then set maximizing the CRB as the criterion for jamming resource allocation, aiming at degrading the accuracy of target parameters estimation. Due to the non-convexity of the CRB maximizing problem, particle swarm optimization is used to solve the problem. Simulation results demonstrate the advantages of the proposed strategy over traditional jamming methods. MDPI 2019-05-29 /pmc/articles/PMC6603513/ /pubmed/31146361 http://dx.doi.org/10.3390/s19112453 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 Zheng, Guangyong Na, Siqi Huang, Tianyao Wang, Lulu A Barrage Jamming Strategy Based on CRB Maximization against Distributed MIMO Radar |
title | A Barrage Jamming Strategy Based on CRB Maximization against Distributed MIMO Radar |
title_full | A Barrage Jamming Strategy Based on CRB Maximization against Distributed MIMO Radar |
title_fullStr | A Barrage Jamming Strategy Based on CRB Maximization against Distributed MIMO Radar |
title_full_unstemmed | A Barrage Jamming Strategy Based on CRB Maximization against Distributed MIMO Radar |
title_short | A Barrage Jamming Strategy Based on CRB Maximization against Distributed MIMO Radar |
title_sort | barrage jamming strategy based on crb maximization against distributed mimo radar |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6603513/ https://www.ncbi.nlm.nih.gov/pubmed/31146361 http://dx.doi.org/10.3390/s19112453 |
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