Comprehensive Survey of Deep Learning in Radar Signal Processing: Opportunities and Challenges
Keywords:
Deep learning, Waveform recognition, Automatic target recognition (ATR), Low probability of interceptAbstract
As of the most important branches of artificial intelligence, deep learning (DL) has developed rapidly in recent years, and has been successfully used in many research fields. Although the DL-based algorithms offer a great opportunity for researchers to finally conquer the bottleneck problems in the field of radar signal processing, they also bring about brand-new technical challenges. In this paper, comprehensive review of the applications of DL methods is proposed, including low probability of interception and passive radar waveform recognition, automatic target recognition, radar jamming/clutter recognition and suppression, and radar waveform and antenna array design. Recently, the proposed DL-based radar waveform recognition and SAR automatic target recognition methods are summarized and analyzed in detail. The major factors limiting the performance of the DL algorithms are also examined. This work aims to provide valuable information to the scholars in this promising field of research.
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