Deep Learning-Based Approach for Low Probability of Intercept Radar Signal Detection and Classification

Ghadimi, G.; Norouzi, Yaser; Bayderkhani, Reza; Nayebi, Mohammad Mahdi; Karbasi, Seyed Mohammad

doi:10.1134/s1064226920100034

Cited by 31 publications

(26 citation statements)

References 18 publications

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“…To select a learning framework to evaluate the recognition performance of the proposed method, comparative experiments were performed on five different learning models including PCA based SVM, because PCA based SVM method is widely used in the motion classification. In this paper, the learning frameworks considered are GoogleNet, ResNet, VGG, AlexNet, and PCA based SVM [ 34 , 39 , 40 , 51 , 52 , 53 ]. Comparative recognition performance is summarized in Table 2 .…”

Section: Recognition Resultsmentioning

confidence: 99%

“…On the other hand, deep learning approaches based on multi-layer networks such as the convolutional neural network (CNN) are promising for overcoming such feature selection problems without advance need for feature sets. GoogleNet, AlexNet, VGGNet, ResNet, and DenseNet are good examples of deep learning models [ 34 , 35 , 36 , 37 , 38 , 39 , 40 ].…”

Section: Introductionmentioning

confidence: 99%

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Foot Gesture Recognition Using High-Compression Radar Signature Image and Deep Learning

Song

Kim

et al. 2021

Sensors

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Recently, Doppler radar-based foot gesture recognition has attracted attention as a hands-free tool. Doppler radar-based recognition for various foot gestures is still very challenging. So far, no studies have yet dealt deeply with recognition of various foot gestures based on Doppler radar and a deep learning model. In this paper, we propose a method of foot gesture recognition using a new high-compression radar signature image and deep learning. By means of a deep learning AlexNet model, a new high-compression radar signature is created by extracting dominant features via Singular Value Decomposition (SVD) processing; four different foot gestures including kicking, swinging, sliding, and tapping are recognized. Instead of using an original radar signature, the proposed method improves the memory efficiency required for deep learning training by using a high-compression radar signature. Original and reconstructed radar images with high compression values of 90%, 95%, and 99% were applied for the deep learning AlexNet model. As experimental results, movements of all four different foot gestures and of a rolling baseball were recognized with an accuracy of approximately 98.64%. In the future, due to the radar’s inherent robustness to the surrounding environment, this foot gesture recognition sensor using Doppler radar and deep learning will be widely useful in future automotive and smart home industry fields.

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Section: Recognition Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Foot Gesture Recognition Using High-Compression Radar Signature Image and Deep Learning

Song

Kim

et al. 2021

Sensors

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“…In literatures, dozens of DL-based waveform recognition techniques have been proposed within the past five years. Usually, the raw radar data are first preprocessed with time-frequency analysis (TFA) techniques, such as Choi-William distribution (CWD) [30]- [35], Fourier-based Synchrosqueezing transform (FSST) [36], Wigner Ville distribution (WVD) [37], and short-time Fourier transform (STFT) [38]- [40], to obtain the timefrequency images. After that, various DNN structures, mostly CNN, could be designed for feature extraction and waveform classification.…”

Section: A DL For Lpi Radarmentioning

confidence: 99%

“…In [37], the WVD was adopted, and a VGG16 variant pretrained with ImageNet was used to reduce the training cost. Moreover, the STFT was adopted in [38]- [40] to obtain the time-frequency diagram of radar data. In [38], Ghadimi et al proposed two CNN structures based the GoogLeNet and AlexNet, respectively, for the classification of LFM, P2-P4, and T1-T4 waveforms.…”

Section: A DL For Lpi Radarmentioning

confidence: 99%

“…Moreover, the STFT was adopted in [38]- [40] to obtain the time-frequency diagram of radar data. In [38], Ghadimi et al proposed two CNN structures based the GoogLeNet and AlexNet, respectively, for the classification of LFM, P2-P4, and T1-T4 waveforms. In [39], Wei et al proposed a novel squeeze-and-excitation network for feature extraction in time, frequency, and time-frequency domains, and the recognition results of all the domains are fused subsequently.…”

Section: A DL For Lpi Radarmentioning

confidence: 99%

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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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A method for LPI radar signals recognition based on complex convolutional neural network

Liu

Wang²,

et al. 2023

Int J Numerical Modelling

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For the low probability of intercept (LPI) radar signal recognition problem, recognition algorithms based on deep learning usually use time‐frequency analysis to convert the signal into a two‐dimensional feature image for classification and recognition. However, these methods often have problems with large network size, high computational complexity, and huge memory consumption, making them difficult to apply on small devices with limited computational power and storage space. This paper proposes an LPI radar signal recognition method based on a lightweight complex convolutional neural network, named CV‐LPINet. It uses a complex convolutional module to complete data fusion of IQ sampled signals, uses a deep separable convolutional module to extract features and reduce dimensions, and introduces residual structures to improve network training. Experiments show that the average recognition accuracy of this method is 91.76% with a signal to noise ratio in the range of −6 to 10 dB, and its recognition accuracy is similar to that of typical algorithms. However, the network size is significantly reduced and the computational complexity is low, making it suitable for small intelligent devices.

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Deep Learning-Based Approach for Low Probability of Intercept Radar Signal Detection and Classification

Cited by 31 publications

References 18 publications

Foot Gesture Recognition Using High-Compression Radar Signature Image and Deep Learning

Foot Gesture Recognition Using High-Compression Radar Signature Image and Deep Learning

Deep-Learning for Radar: A Survey

A method for LPI radar signals recognition based on complex convolutional neural network

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