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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Vydané v:IEEE transactions on aerospace and electronic systems Ročník 61; číslo 2; s. 3306 - 3322
Hlavní autori: Yan, Yujia, Hu, Cheng, Cai, Jiong, Li, Weidong, Yu, Teng, Wang, Rui
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.04.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
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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Popis
Shrnutí: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.
Bibliografia:ObjectType-Article-1
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content type line 14
ISSN:0018-9251
1557-9603
DOI:10.1109/TAES.2024.3487138