Radar emitters classification and clustering with a scale mixture of normal distributions

Revillon, Guillaume; Mohammad-Djafari, Ali; Enderli, Cyrille

doi:10.1109/radar.2018.8378764

Cited by 10 publications

(8 citation statements)

References 24 publications

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“…Most existing literature was motivated to classify different communication signals for channel sensing and spectrum allocation [6,11,33,34]. Besides, different machine learning methods were explored to classify civil radars and military radars [7,29,31]. However, none of work consider that different types of radar have different applications and work to classify them based on their types.…”

Section: Related Workmentioning

confidence: 99%

$Radar^2$: Passive Spy Radar Detection and Localization using COTS mmWave Radar

Qiu¹,

Zhang²,

Wei³

et al. 2022

Preprint

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Millimeter-wave (mmWave) radars have found applications in a wide range of domains, including human tracking, health monitoring, and autonomous driving, for its unobtrusive nature and high range accuracy. These capabilities, however, if used for malicious purposes, could also lead to serious security and privacy issues. For example, a user's daily life could be secretly monitored by a spy radar. Hence, there is a strong urge to develop systems that can detect and localize such spy radars. In this paper, we propose 𝑅𝑎𝑑𝑎𝑟 2 , a practical passive spy radar detection and localization system using a single commercial off-the-shelf (COTS) mmWave radar. Specifically, we propose a novel Frequency Component Detection method to detect the existence of mmWave signal, distinguish between mmWave radar and WiGig signals using a convolutional neural network (CNN) based waveform classifier, and localize spy radars using the trilateration method based on the detector's observations at multiple anchor points. Not only does 𝑅𝑎𝑑𝑎𝑟 2 work for different types of mmWave radar, but it can also detect and localize multiple radars simultaneously. Finally, we perform extensive experiments to evaluate the effectiveness and robustness of 𝑅𝑎𝑑𝑎𝑟 2

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Section: Related Workmentioning

confidence: 99%

$Radar^2$: Passive Spy Radar Detection and Localization using COTS mmWave Radar

Qiu¹,

Zhang²,

Wei³

et al. 2022

Preprint

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“…In recent years, because of the high-efficiency of ML algorithms and the rapid development of novel RSP technology, ML-based methods have been successfully applied to RRSCR to face some critical challenges. To better understand these research developments and grasp future research directions in this domain, we provide a comprehensive survey on ML- PWDs [170], [175], [176], [179];entropy theory [161]; spectrum features [112], [171], [177]; wavelet packets [172]; dynamic parameters searching [173]; rough sets [174]; energy envelope [181]; time-frequency analysis [185]- [188]; autocorrelation images [113], [156], [189], [190]; CWTFD [114]- [117]; PCA [191], ambiguity function images [125] SVMs [170], [192]- [194], [196]; ANNs [161], [170], [171], [182], [197]- [202]; DT [118], [119], [203]; RF [170]; Adaboost [120]; clustering [121]- [124], [161]; K-NN [126]- [129]; weighted-Xgboost [180]; HMMs…”

Section: Radar Radiation Sources Classification and Recognitionmentioning

confidence: 99%

“…In addition, other classification learning models were also researched in RRSCR, such as DT [203], RF [170], weighted-XGBoost [180], Hidden Markov Models (HMMs) [204], Adaboost [120], clustering [121]- [124], [161], K-NN [126]- [129].…”

Section: B Traditional Machine Learning In Rrscrmentioning

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, the increasing complexity of the electromagnetic environment makes the task of SEI more and more difficult. For example, a low signal-to-noise ratio (SNR) environment may increase measurement loss or error, resulting in poor performance [4]. In order to suppress the influence of noise and improve the recognition accuracy, extensive research has been performed, such as using compressed sensing reconstruction algorithms to recover structured signals [5][6][7][8], as well as using complex-valued neural networks to improve the anti-noise performance [9].…”

Section: Introductionmentioning

confidence: 99%

A Complex-Valued Self-Supervised Learning-Based Method for Specific Emitter Identification

Zhao

Yang

Liu

et al. 2022

Entropy

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Specific emitter identification (SEI) refers to distinguishing emitters using individual features extracted from wireless signals. The current SEI methods have proven to be accurate in tackling large labeled data sets at a high signal-to-noise ratio (SNR). However, their performance declines dramatically in the presence of small samples and a significant noise environment. To address this issue, we propose a complex self-supervised learning scheme to fully exploit the unlabeled samples, comprised of a pretext task adopting the contrastive learning concept and a downstream task. In the former task, we design an optimized data augmentation method based on communication signals to serve the contrastive conception. Then, we embed a complex-valued network in the learning to improve the robustness to noise. The proposed scheme demonstrates the generality of handling the small and sufficient samples cases across a wide range from 10 to 400 being labeled in each group. The experiment also shows a promising accuracy and robustness where the recognition results increase at 10–16% from 10–15 SNR.

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Radar emitters classification and clustering with a scale mixture of normal distributions

Cited by 10 publications

References 24 publications

$Radar^2$: Passive Spy Radar Detection and Localization using COTS mmWave Radar

$Radar^2$: Passive Spy Radar Detection and Localization using COTS mmWave Radar

A Comprehensive Survey of Machine Learning Applied to Radar Signal Processing

A Complex-Valued Self-Supervised Learning-Based Method for Specific Emitter Identification

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