Research on Online Learning of Radar Emitter Recognition Based on Hull Vector

Zhu, Wei; Li, Meng; Zeng, Chuangzhan

doi:10.1109/dsc.2017.30

Cited by 5 publications

(4 citation statements)

References 3 publications

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“…As shown in Table 3, test accuracies of SVM, NN and DT were close to each other, which varied in the range 94.1% to 95.5%. In [4–6], SVM achieved quite good performance on small datasets. However, in our case of big data, it was not the case.…”

Section: Experiments and Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Radar emitter classification based on unidimensional convolutional neural network

Sun

Ren

et al. 2018

IET Radar, Sonar & Navigation

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Section: Experiments and Resultsmentioning

confidence: 99%

“…Jordanov et al [4] utilised SVM model in REC to handle incomplete samples. Zhu et al [5] realised an online learning SVM model based on hull vector, which improved SVM's ability to learn new knowledge. Xu et al [6] designed a novel method for estimating kernels of SVM, and it was used to choose the best kernel for different REC tasks.…”

Section: Introductionmentioning

confidence: 99%

Radar emitter classification based on unidimensional convolutional neural network

Sun

Ren

et al. 2018

IET Radar, Sonar & Navigation

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“…However, SVM classifier does not good at learning new knowledge in real-time. Hull vector and Parzen window density estimation [195] were reported for online learning of radar emitter recognition.…”

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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“…According to the supplied features, these studies mainly concentrate on the time domain features [ 10 , 12 , 13 ], transformed domain features [ 10 , 11 , 12 , 13 , 14 ] and statistics features [ 15 ]. From the aspect of the recognition method, the studies can be divided into two groups: classifiers based on multi-thresholds and multi-regulations [ 10 , 12 ], and classifiers based on learning algorithms [ 16 , 17 , 18 , 19 , 20 , 21 ]. However, it is one-sided to divide the radar emitter recognition only discussing the recognition features or recognition methods separately, because the recognition features are related to the recognition methods, and the length, dimension, structure and format of the features have decided the optimal recognition methods, so it is a good idea for radar emitter recognition method categories to be defined by combining the features and methods together as follows.…”

Section: Introductionmentioning

confidence: 99%

Radar Emitter Recognition Based on the Energy Cumulant of Short Time Fourier Transform and Reinforced Deep Belief Network

Wang

Huang

Zhou

et al. 2018

Sensors

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To cope with the complex electromagnetic environment and varied signal styles, a novel method based on the energy cumulant of short time Fourier transform and reinforced deep belief network is proposed to gain a higher correct recognition rate for radar emitter intra-pulse signals at a low signal-to-noise ratio. The energy cumulant of short time Fourier transform is attained by calculating the accumulations of each frequency sample value with the different time samples. Before this procedure, the time frequency distribution via short time Fourier transform is processed by base noise reduction. The reinforced deep belief network is proposed to employ the input feature vectors for training to achieve the radar emitter recognition and classification. Simulation results manifest that the proposed method is feasible and robust in radar emitter recognition even at a low SNR.

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Research on Online Learning of Radar Emitter Recognition Based on Hull Vector

Cited by 5 publications

References 3 publications

Radar emitter classification based on unidimensional convolutional neural network

Radar emitter classification based on unidimensional convolutional neural network

A Comprehensive Survey of Machine Learning Applied to Radar Signal Processing

Radar Emitter Recognition Based on the Energy Cumulant of Short Time Fourier Transform and Reinforced Deep Belief Network

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