BRSR-OpGAN: Blind Radar Signal Restoration using Operational Generative Adversarial Network
Muhammad Uzair Zahid, Serkan Kiranyaz, Alper Yildirim, and Moncef, Gabbouj

TL;DR
This paper introduces BRSR-OpGAN, a novel generative adversarial network designed for blind radar signal restoration that effectively handles diverse artifacts and noise types in real-world conditions.
Contribution
The paper presents a dual domain loss GAN model that adapts to various artifact types and severities, improving radar signal quality in complex environments.
Findings
Achieved over 15 dB SNR improvement on benchmark datasets.
Effective real-time restoration on resource-limited platforms.
Validated on a new comprehensive dataset simulating real-world artifacts.
Abstract
Objective: Many studies on radar signal restoration in the literature focus on isolated restoration problems, such as denoising over a certain type of noise, while ignoring other types of artifacts. Additionally, these approaches usually assume a noisy environment with a limited set of fixed signal-to-noise ratio (SNR) levels. However, real-world radar signals are often corrupted by a blend of artifacts, including but not limited to unwanted echo, sensor noise, intentional jamming, and interference, each of which can vary in type, severity, and duration. This study introduces Blind Radar Signal Restoration using an Operational Generative Adversarial Network (BRSR-OpGAN), which uses a dual domain loss in the temporal and spectral domains. This approach is designed to improve the quality of radar signals, regardless of the diversity and intensity of the corruption. Methods: The BRSR-OpGAN…
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Taxonomy
TopicsGeophysical Methods and Applications · Underwater Acoustics Research · Advanced SAR Imaging Techniques
MethodsSparse Evolutionary Training · Focus
