2023
DOI: 10.1088/1742-6596/2486/1/012076
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Track Detection of Underwater Moving Targets Based on CFAR

Abstract: In this paper, we propose a 2D-Weibull-Constant False Alarm Rate (2D-Weibull-CFAR) detection algorithm to solve the problem that detecting current underwater targets is difficult due to the influence of reverberation noise. Specifically, referring to the idea that CFAR uses the probability distribution of reference units to detect objects, this paper introduces the pixel distribution of reverberation noise into the CFAR detector. After that, the probability distribution of the extracted reference units is esti… Show more

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Cited by 2 publications
(2 citation statements)
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“…The optimal model uses the two-tower recall model to expand negative samples. The computational advantage of the two-tower model lies in the use of negative samples in batch samples to reduce the computational amount [34]. If we want to increase the number of samples in the batch, and increase the number of negative samples, we need more memory.…”
Section: Increase the Negative Samplementioning
confidence: 99%
“…The optimal model uses the two-tower recall model to expand negative samples. The computational advantage of the two-tower model lies in the use of negative samples in batch samples to reduce the computational amount [34]. If we want to increase the number of samples in the batch, and increase the number of negative samples, we need more memory.…”
Section: Increase the Negative Samplementioning
confidence: 99%
“…Underwater target detection and tracking in active sonar systems has always been a hot topic in underwater applications. The conventional approach to detect and track underwater targets involves threshold detection, followed by data association and filtering tracking [1][2][3][4]. However, practical sonar systems often encounter strong reverberation interference.…”
Section: Introductionmentioning
confidence: 99%