Abstract
A comprehensive and well-structured review on the application of deep learning (DL) based algorithms, such as convolutional neural networks (CNN) and long-short term memory (LSTM), in radar signal processing is given. The following DL application areas are covered: i) radar waveform and antenna array design; ii) passive or low probability of interception (LPI) radar waveform recognition; iii) automatic target recognition (ATR) based on high range resolution profiles (HRRPs), Doppler signatures, and synthetic aperture radar (SAR) images; and iv) radar jamming/clutter recognition and suppression. Although DL is unanimously praised as the ultimate solution to many bottleneck problems in most of existing works on similar topics, both the positive and the negative sides of stories about DL are checked in this work. Specifically, two limiting factors of the real-life performance of deep neural networks (DNNs), limited training samples and adversarial examples, are thoroughly examined. By investigating the relationship between the DL-based algorithms proposed in various papers and linking them together to form a full picture, this work serves as a valuable source for researchers who are seeking potential research opportunities in this promising research field.
Highlights
In recent years, top researchers around the world have been increasingly resorting to deep learning (DL) based algorithms to solve bottle-neck problems in the field of radar signal processing [1], [2]
In [4], Zhu et al provided a comprehensive review on deep learning in remote sensing, which is focused on automatic target recognition (ATR) and terrain surface classification based on synthetic aperture radar (SAR) images
In [5], Zhang et al presented a technical tutorial on the advances in deep learning for remote sensing and geosciences, which is focused on image classification
Summary
Top researchers around the world have been increasingly resorting to deep learning (DL) based algorithms to solve bottle-neck problems in the field of radar signal processing [1], [2]. The amount of publications on “deep learning for radar” have been increasing rapidly. To illustrate radar engineers’ soaring interests in DL, the number of publications on the topic of “deep learning for radar” from 2016 to 2020 are plotted in Fig. 1 (IEEE Xplore database). A comprehensive survey of machine learning algorithms applied to radar signal processing is given in [3], where six aspects are considered: i) radar radiation sources. We conduct a comprehensive review on the application of DL-based algorithms in radar signal processing, which includes the following aspects: a) DL for waveform and array design, which is an enabling technology for cognitive radar & spectrum sharing; b) DL-based radar waveform recognition, which could potentially 1) boost the possibility of intercepting and recognizing the signals transmitted from the low probability of interception (LPI) radar; and 2) improve.
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