CFAR Algorithm Based on Different Probability Models for Ocean Target Detection

The two-parameter constant false alarm rate (CFAR) detection algorithm uses the background average <inline-formula> <tex-math notation="LaTeX">$u_{b}$ </tex-math></inline-formula> and the standard deviation <inline-formula> <tex-math notation="LaTeX&qu...

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Autores principales:	Wanwu Li, Jixian Zhang, Lin Liu, Jiaxing Zhou, Qiaoli Sui, Hang Li
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Ocean target detection two-parameter CFAR loglogistic distribution deep learning model initial detection Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/c0c9895900f341a3ba28e59e2cf4fe96
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spelling	oai:doaj.org-article:c0c9895900f341a3ba28e59e2cf4fe962021-11-24T00:02:47ZCFAR Algorithm Based on Different Probability Models for Ocean Target Detection2169-353610.1109/ACCESS.2021.3126003https://doaj.org/article/c0c9895900f341a3ba28e59e2cf4fe962021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9605628/https://doaj.org/toc/2169-3536The two-parameter constant false alarm rate (CFAR) detection algorithm uses the background average <inline-formula> <tex-math notation="LaTeX">$u_{b}$ </tex-math></inline-formula> and the standard deviation <inline-formula> <tex-math notation="LaTeX">$\sigma _{b}$ </tex-math></inline-formula> to determine the target detection threshold, which is simple and easy to implement. However, it is limited by the assumption of Gaussian distribution. Based on different probability distributions, the research improves the two-parameter CFAR algorithm, and proposes the two-parameter CFAR method based on initial detection, and the detection methods based on Loglogistic Distribution model and Adjoint Covariance Correction Model (ACCM). Three methods are used to detect and extract ocean targets in the same research area, and the results are compared and analyzed. The experimental results show that ACCM proposed in the research fits the long tail characteristic of the ocean background under complex ocean conditions well. Its goodness of fit is improved by nearly 50% compared with Loglogistic Distribution model, and its amount of false alarm of ocean target detection is 77.78% of Loglogistic model. In addition, in view of a large amount of calculation caused by the sliding window of the traditional two-parameter CFAR, OceanTDA9 deep learning model is designed for initial detection in research, which improves the detection speed of ocean targets.Wanwu LiJixian ZhangLin LiuJiaxing ZhouQiaoli SuiHang LiIEEEarticleOcean target detectiontwo-parameter CFARloglogistic distributiondeep learning modelinitial detectionElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 154355-154367 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Ocean target detection two-parameter CFAR loglogistic distribution deep learning model initial detection Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Ocean target detection two-parameter CFAR loglogistic distribution deep learning model initial detection Electrical engineering. Electronics. Nuclear engineering TK1-9971 Wanwu Li Jixian Zhang Lin Liu Jiaxing Zhou Qiaoli Sui Hang Li CFAR Algorithm Based on Different Probability Models for Ocean Target Detection
description	The two-parameter constant false alarm rate (CFAR) detection algorithm uses the background average <inline-formula> <tex-math notation="LaTeX">$u_{b}$ </tex-math></inline-formula> and the standard deviation <inline-formula> <tex-math notation="LaTeX">$\sigma _{b}$ </tex-math></inline-formula> to determine the target detection threshold, which is simple and easy to implement. However, it is limited by the assumption of Gaussian distribution. Based on different probability distributions, the research improves the two-parameter CFAR algorithm, and proposes the two-parameter CFAR method based on initial detection, and the detection methods based on Loglogistic Distribution model and Adjoint Covariance Correction Model (ACCM). Three methods are used to detect and extract ocean targets in the same research area, and the results are compared and analyzed. The experimental results show that ACCM proposed in the research fits the long tail characteristic of the ocean background under complex ocean conditions well. Its goodness of fit is improved by nearly 50% compared with Loglogistic Distribution model, and its amount of false alarm of ocean target detection is 77.78% of Loglogistic model. In addition, in view of a large amount of calculation caused by the sliding window of the traditional two-parameter CFAR, OceanTDA9 deep learning model is designed for initial detection in research, which improves the detection speed of ocean targets.
format	article
author	Wanwu Li Jixian Zhang Lin Liu Jiaxing Zhou Qiaoli Sui Hang Li
author_facet	Wanwu Li Jixian Zhang Lin Liu Jiaxing Zhou Qiaoli Sui Hang Li
author_sort	Wanwu Li
title	CFAR Algorithm Based on Different Probability Models for Ocean Target Detection
title_short	CFAR Algorithm Based on Different Probability Models for Ocean Target Detection
title_full	CFAR Algorithm Based on Different Probability Models for Ocean Target Detection
title_fullStr	CFAR Algorithm Based on Different Probability Models for Ocean Target Detection
title_full_unstemmed	CFAR Algorithm Based on Different Probability Models for Ocean Target Detection
title_sort	cfar algorithm based on different probability models for ocean target detection
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/c0c9895900f341a3ba28e59e2cf4fe96
work_keys_str_mv	AT wanwuli cfaralgorithmbasedondifferentprobabilitymodelsforoceantargetdetection AT jixianzhang cfaralgorithmbasedondifferentprobabilitymodelsforoceantargetdetection AT linliu cfaralgorithmbasedondifferentprobabilitymodelsforoceantargetdetection AT jiaxingzhou cfaralgorithmbasedondifferentprobabilitymodelsforoceantargetdetection AT qiaolisui cfaralgorithmbasedondifferentprobabilitymodelsforoceantargetdetection AT hangli cfaralgorithmbasedondifferentprobabilitymodelsforoceantargetdetection
_version_	1718416118631104512

CFAR Algorithm Based on Different Probability Models for Ocean Target Detection

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