A Segmentation-Based CFAR Detector With Spatial Continuity Constraint in Nonhomogeneous Weather Clutter

The performance of conventional constant false alarm rate (CFAR) detectors may degrade in nonhomogeneous clutter environments, as accurately estimating the clutter distribution in the cell under test (CUT) using reference cells becomes challenging. In this article, a CFAR detector based on clutter s...

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Vydáno v:	IEEE transactions on aerospace and electronic systems Ročník 61; číslo 2; s. 3306 - 3322
Hlavní autoři:	Yan, Yujia, Hu, Cheng, Cai, Jiong, Li, Weidong, Yu, Teng, Wang, Rui
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 01.04.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Aerospace and electronic systems Algorithms Clutter Clutter segmentation Constant false alarm rate Constraints Detectors False alarms Fields (mathematics) Hidden Markov models hidden Markov random field (HMRF) Meteorology Mixture models Parameter estimation Phased arrays Probabilistic models Radar arrays Radar clutter Radar detection Rain Segmentation Simulation Target detection variational expectation-maximization algorithm Weather weather clutter
ISSN:	0018-9251, 1557-9603
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Abstract	The performance of conventional constant false alarm rate (CFAR) detectors may degrade in nonhomogeneous clutter environments, as accurately estimating the clutter distribution in the cell under test (CUT) using reference cells becomes challenging. In this article, a CFAR detector based on clutter segmentation with spatial continuity constraints is proposed for target detection within nonhomogeneous weather clutter backgrounds. Analysis of real weather clutter collected by a high-resolution phased array radar indicates that the Rayleigh mixture model can precisely characterize the amplitude distribution of nonhomogeneous weather clutter in spatial domain. The hidden Markov random field model is employed to capture the spatial correlation of weather clutter. Based on this model, clutter segmentation is implemented using the variational expectation-maximization algorithm, which provides the posterior class of clutter in each range cell and the estimated parameter of each class. Simulation results indicate that introducing the spatial continuity improves the accuracy of clutter segmentation and parameter estimation. A CFAR detection scheme is proposed, which utilizes the segmentation results to estimate the clutter distribution of the CUT and set the detection threshold accordingly. Experiments conducted using both simulated data and real weather clutter have demonstrated that the proposed method improves detection performance. The proposed method exhibit a maximum increase in detection probability of 8.97% compared to the best-performing benchmark method when the false alarm rate is 10^{-6} in real weather clutter.
AbstractList	The performance of conventional constant false alarm rate (CFAR) detectors may degrade in nonhomogeneous clutter environments, as accurately estimating the clutter distribution in the cell under test (CUT) using reference cells becomes challenging. In this article, a CFAR detector based on clutter segmentation with spatial continuity constraints is proposed for target detection within nonhomogeneous weather clutter backgrounds. Analysis of real weather clutter collected by a high-resolution phased array radar indicates that the Rayleigh mixture model can precisely characterize the amplitude distribution of nonhomogeneous weather clutter in spatial domain. The hidden Markov random field model is employed to capture the spatial correlation of weather clutter. Based on this model, clutter segmentation is implemented using the variational expectation-maximization algorithm, which provides the posterior class of clutter in each range cell and the estimated parameter of each class. Simulation results indicate that introducing the spatial continuity improves the accuracy of clutter segmentation and parameter estimation. A CFAR detection scheme is proposed, which utilizes the segmentation results to estimate the clutter distribution of the CUT and set the detection threshold accordingly. Experiments conducted using both simulated data and real weather clutter have demonstrated that the proposed method improves detection performance. The proposed method exhibit a maximum increase in detection probability of 8.97% compared to the best-performing benchmark method when the false alarm rate is 10^{-6} in real weather clutter.
Author	Cai, Jiong Yu, Teng Li, Weidong Yan, Yujia Hu, Cheng Wang, Rui
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SubjectTerms	Aerospace and electronic systems Algorithms Clutter Clutter segmentation Constant false alarm rate Constraints Detectors False alarms Fields (mathematics) Hidden Markov models hidden Markov random field (HMRF) Meteorology Mixture models Parameter estimation Phased arrays Probabilistic models Radar arrays Radar clutter Radar detection Rain Segmentation Simulation Target detection variational expectation-maximization algorithm Weather weather clutter
Title	A Segmentation-Based CFAR Detector With Spatial Continuity Constraint in Nonhomogeneous Weather Clutter
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