A Novel Algorithm of Radar Emitter Identification Based Convolutional Neural Network and Random Vector Functional-Link

Zhou, Zhiwen; Zhang, Jingke; Zhang, Taotao

doi:10.1007/978-3-030-32591-6_90

Cited by 1 publication

(2 citation statements)

References 14 publications

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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%

“…In order to accelerate feature learning of CNN, a PCA based CNN architecture was proposed in [231] to reduce dimensionality of TFD images. After feature extraction with CNN, random vector functional link (RVFL) was employed in [233] to promote feature learning ability, and picked out the maximum of RVFL as identification results of signals.…”

Section: Deep Learning In Rrscrmentioning

confidence: 99%

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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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