Ambiguity Function Based Radar Waveform Classification and Unsupervised Adaptation Using Deep CNN Models

Itkin, Pavel; Levanon, Nadav

doi:10.1109/comcas44984.2019.8958242

Cited by 5 publications

(4 citation statements)

References 21 publications

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“…The DL-based radar waveform recognition is also gaining popularity in recent years. Various neural networks and algorithms have been developed, which include the deep convolutional neural networks (CNNs) [21]- [23], autoencoders [24]- [26], and recurrent neural networks (RNNs) [27]- [29]. These techniques could potentially 1) boost the possibility of intercepting and recognizing the signals transmitted from the low probability of interception (LPI) radar [30]- [31]; and 2) improve the direct-path signal estimation accuracy for passive radar applications [43]- [45].…”

Section: DL For Lpi or Passive Radar Waveform Recognitionmentioning

confidence: 99%

Deep-Learning for Radar: A Survey

Geng¹,

Zhang

et al. 2021

IEEE Access

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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.

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Section: DL For Lpi or Passive Radar Waveform Recognitionmentioning

confidence: 99%

Deep-Learning for Radar: A Survey

Geng¹,

Zhang

et al. 2021

IEEE Access

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“…IQ 1D time sequences [138], [210], [218], [569]; STFT [133]- [135], [137], [212], [229], [230]; CWTFD [130], [215], [217]- [219], [227]; amplitude-phase shift [211]; CTFD [131], [221], [222]; bivariate image with FST [132]; bispectrum [237]; autocorrelation features [213]- [215]; ambiguity function images [140], [141]; fusion features [139], [220] CNNs [82], [210], [211], [217]- [222], [228]- [231], [233], [237], [569]; RNNs [142]- [144], [216]; DBNs [135], [136], [235], [236]; AEs [222]; SENet [212], [213]; ACSENet…”

Section: Features Models Accuracymentioning

confidence: 99%

“…The former are encoded IQ time sequences [138], [210], [218], [569], and the latter usually are time-frequency distribution (TFD) images, which are produced by short time fourier transformation (STFT) [212], Choi-Williams time-frequency distribution (CWTFD) [217], [218], and Cohen's time-frequency distribution (CTFD) image [221], [222]. In addition, there are some other two-dimension feature images, such as amplitudephase shift image [211], the spectrogram of the time domain waveform based on STFT [230], bispectrum of signals [237], ambiguity function images [140], [141], and autocorrelation function (ACF) features [213]- [215].…”

Section: A Preprocessingmentioning

confidence: 99%

A Comprehensive Survey of Machine Learning Applied to Radar Signal Processing

Lang,

Fu,

Martorella

et al. 2020

Preprint

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Modern radar systems have high requirements in terms of accuracy, robustness and real-time capability when operating on increasingly complex electromagnetic environments. Traditional radar signal processing (RSP) methods have shown some limitations when meeting such requirements, particularly in matters of target classification. With the rapid development of machine learning (ML), especially deep learning, radar researchers have started integrating these new methods when solving RSP-related problems. This paper aims at helping researchers and practitioners to better understand the application of ML techniques to RSP-related problems by providing a comprehensive, structured and reasoned literature overview of ML-based RSP techniques. This work is amply introduced by providing general elements of ML-based RSP and by stating the motivations behind them. The main applications of ML-based RSP are then analysed and structured based on the application field. This paper then concludes with a series of open questions and proposed research directions, in order to indicate current gaps and potential future solutions and trends.

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“…However, in recent years, 1D CNNs have achieved the state-of-the-art performance levels in many applications such as personalized biomedical data classification and early diagnosis [12]- [15], structural health monitoring [16]- [20], anomaly detection and identification in power electronics and motor-fault detection [21]- [26]. Recently conventional, 2D, and deep CNNs have been applied to radar signal classification [27]- [32]. However, they are very complex and data-hungry models, which require a certain parallelized computing environment and also 1D to 2D transformation of the radar signal.…”

Section: Introductionmentioning

confidence: 99%

1D Convolutional Neural Networks Versus Automatic Classifiers for Known LPI Radar Signals Under White Gaussian Noise

Yıldırım

Kıranyaz

2020

IEEE Access

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In this study we analyze the signal classification performances of various classifiers for deterministic signals under the additive White Gaussian Noise (WGN) in a wide range of signal to noise ratio (SNR) levels (-40dB to +20dB). The traditional electronic support measure (ESM) systems require high SNR for radar signal classification. LPI (low probability of intercept) radar signals that are received by ESM systems are usually corrupted by noise. So, we demonstrate through extensive simulations that it is possible to achieve high classification performance at low SNR levels providing that the underlying radar signals are known in advance. MF bank classifier, 1D Convolutional Neural Networks (CNNs) and the minimum distance classifier using spectral-domain features (the skewness, the kurtosis, and the energy of the dominant frequency) have been derived for the radar signal classification and their performances have been compared with each other and with the optimal classifier.

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Ambiguity Function Based Radar Waveform Classification and Unsupervised Adaptation Using Deep CNN Models

Cited by 5 publications

References 21 publications

Deep-Learning for Radar: A Survey

Deep-Learning for Radar: A Survey

A Comprehensive Survey of Machine Learning Applied to Radar Signal Processing

1D Convolutional Neural Networks Versus Automatic Classifiers for Known LPI Radar Signals Under White Gaussian Noise

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