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A Novel Copula-Based Multi-Feature CFAR Framework for Radar Target Detection.

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Title: A Novel Copula-Based Multi-Feature CFAR Framework for Radar Target Detection.
Authors: Li, Juan1 (AUTHOR), Dong, Yunlong2 (AUTHOR), Liu, Ningbo2,3 (AUTHOR) lnb198300@163.com, Huang, Yong1,2 (AUTHOR), Jiang, Xingyu2 (AUTHOR), Sun, Jinping3 (AUTHOR)
Source: Remote Sensing. Jul2025, Vol. 17 Issue 13, p2299. 21p.
Subjects: Radar, Constant false alarm rate (Data processing), Copula functions, Signal detection, Probability theory, Computer simulation
Abstract: Multi-feature radar target detection enhances the discrimination between targets and clutter, thereby improving detection accuracy. However, the complex nonlinear dependencies among features present significant challenges for precise control of the false alarm rate (FAR). In this paper, a novel constant false alarm rate (CFAR) framework for multi-feature detection is proposed. First, a Copula-CFAR theorem is established, which models the feature dependence structure and enables the derivation of closed-form expressions for probability of false alarm (PFA) and detection probability across various Copula models. Based on this theory, a multi-feature target detection algorithm is developed to achieve a predefined PFA. Simulation and experimental results validate the effectiveness of the approach. The method outperforms conventional CFAR detectors, including CA-CFAR, OS-CFAR, GO-CFAR, and SO-CFAR. Furthermore, compared to state-of-the-art detectors that utilize three features derived from convex hull, concave hull, convex hull principal component analysis (PCA), and concave hull PCA, the proposed method, which uses only two features, achieves relative improvements of 130.53%, 12.26%, 48.09%, and 34.62%, respectively, at a measured FAR of 0.001. [ABSTRACT FROM AUTHOR]
Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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DbLabel: Engineering Source
An: 186597390
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  Data: A Novel Copula-Based Multi-Feature CFAR Framework for Radar Target Detection.
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  Data: <searchLink fieldCode="AR" term="%22Li%2C+Juan%22">Li, Juan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Dong%2C+Yunlong%22">Dong, Yunlong</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liu%2C+Ningbo%22">Liu, Ningbo</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<i> lnb198300@163.com</i><br /><searchLink fieldCode="AR" term="%22Huang%2C+Yong%22">Huang, Yong</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jiang%2C+Xingyu%22">Jiang, Xingyu</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Sun%2C+Jinping%22">Sun, Jinping</searchLink><relatesTo>3</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Jul2025, Vol. 17 Issue 13, p2299. 21p.
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  Data: <searchLink fieldCode="DE" term="%22Radar%22">Radar</searchLink><br /><searchLink fieldCode="DE" term="%22Constant+false+alarm+rate+%28Data+processing%29%22">Constant false alarm rate (Data processing)</searchLink><br /><searchLink fieldCode="DE" term="%22Copula+functions%22">Copula functions</searchLink><br /><searchLink fieldCode="DE" term="%22Signal+detection%22">Signal detection</searchLink><br /><searchLink fieldCode="DE" term="%22Probability+theory%22">Probability theory</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+simulation%22">Computer simulation</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Multi-feature radar target detection enhances the discrimination between targets and clutter, thereby improving detection accuracy. However, the complex nonlinear dependencies among features present significant challenges for precise control of the false alarm rate (FAR). In this paper, a novel constant false alarm rate (CFAR) framework for multi-feature detection is proposed. First, a Copula-CFAR theorem is established, which models the feature dependence structure and enables the derivation of closed-form expressions for probability of false alarm (PFA) and detection probability across various Copula models. Based on this theory, a multi-feature target detection algorithm is developed to achieve a predefined PFA. Simulation and experimental results validate the effectiveness of the approach. The method outperforms conventional CFAR detectors, including CA-CFAR, OS-CFAR, GO-CFAR, and SO-CFAR. Furthermore, compared to state-of-the-art detectors that utilize three features derived from convex hull, concave hull, convex hull principal component analysis (PCA), and concave hull PCA, the proposed method, which uses only two features, achieves relative improvements of 130.53%, 12.26%, 48.09%, and 34.62%, respectively, at a measured FAR of 0.001. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.3390/rs17132299
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        Text: English
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      – SubjectFull: Radar
        Type: general
      – SubjectFull: Constant false alarm rate (Data processing)
        Type: general
      – SubjectFull: Copula functions
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      – SubjectFull: Signal detection
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      – SubjectFull: Computer simulation
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      – TitleFull: A Novel Copula-Based Multi-Feature CFAR Framework for Radar Target Detection.
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            NameFull: Li, Juan
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            NameFull: Dong, Yunlong
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              M: 07
              Text: Jul2025
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              Y: 2025
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