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• W4384447319 abstract "Passive Bistatic Radar (PBR) has significant civilian and military applications due to its ability to detect low-altitude targets. However, the uncontrollable characteristics of the transmitter often lead to subpar target detection performance, primarily due to a low signal-to-noise ratio (SNR). Coherent accumulation typically has limited ability to improve SNR in the presence of strong noise and clutter. In this paper, we propose an adversarial learning-based radar signal enhancement method, called radar signal enhancement generative adversarial network (RSEGAN), to overcome this challenge. On one hand, an encoder-decoder structure is designed to map noisy signals to clean ones without intervention in the adversarial training stage. On the other hand, a hybrid loss constrained by L1 regularization, L2 regularization, and gradient penalty is proposed to ensure effective training of RSEGAN. Experimental results demonstrate that RSEGAN can reliably remove noise from target information, providing an SNR gain higher than 5 dB for the basic coherent integration method even under low SNR conditions." @default.
• W4384447319 created "2023-07-16" @default.
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• W4384447319 date "2023-07-14" @default.
• W4384447319 modified "2023-09-28" @default.
• W4384447319 title "A Novel Adversarial Learning Framework for Passive Bistatic Radar Signal Enhancement" @default.
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• W4384447319 doi "https://doi.org/10.3390/electronics12143072" @default.
• W4384447319 hasPublicationYear "2023" @default.
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