LPI Radar Waveform Recognition Based on Multi-Resolution Deep Feature Fusion

Xue, Nan; Wang, Huali; Fan, Meng; Hu, Jing; Tong, Changkai

doi:10.1109/access.2021.3058305

Cited by 18 publications

(19 citation statements)

References 22 publications

(34 reference statements)

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“…Designing new features [19,32] Improving the TFD algorithm [15,33] Make the picture clearer [18,29] Improving the classifier Structure expansion [6,7,16,25] Designing the CNN manually [14][15][16] Replacing the fully connected layer (FC) with other structures [20,24,32] Transferring learning [23, 24, 26-31, 33, 34]…”

Section: Improving In Preprocessmentioning

confidence: 99%

“…Compared with other neural networks [5][6][7], the convolutional neural network (CNN) has a better performance in the processing of image, including radar and sonar images, facial images, and hand gesture images [8][9][10]. erefore, it also has been widely used in the recognition of radar waveforms [7,[11][12][13][14][15][16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

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LPI Radar Waveform Recognition Based on Neural Architecture Search

Zhang

et al. 2022

Computational Intelligence and Neuroscience

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In order to reach the intelligent recognition, the deep learning classifiers adopted by radar waveform are normally trained with transfer learning, where the pretrained convolutional neural network on an external large-scale classification dataset (e.g., ImageNet) is used as the backbone. Though transfer learning could effectively avoid overfitting, transferred models are usually redundant and might not generalize well. To eliminate the dependence on transfer learning and achieve high generalization ability, this paper introduced neural architecture search (NAS) to search the suitable classifier of radar waveforms for the first time. Firstly, one of the innovative technologies in NAS called differentiable architecture search (DARTS) was used to design the classifier for 15 kinds of low probability intercept radar waveforms automatically. Then, a method with an auxiliary classifier called flexible-DARTS was proposed. By adding an auxiliary classifier in the middle layer, the flexible-DARTS has a better performance in designing well-generalized classifiers than the standard DARTS. Finally, the performance of the classifier in practical application was compared with related work. Simulation proves that the model based on flexible-DARTS has a better performance, and the accuracy rate for 15 kinds of radar waveforms can reach 79.2% under the −9 dB SNR which proved the effectiveness of the method proposed in this paper for the recognition of radar waveforms.

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Section: Improving In Preprocessmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

LPI Radar Waveform Recognition Based on Neural Architecture Search

Zhang

et al. 2022

Computational Intelligence and Neuroscience

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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%

“…Although the CWD is a widely adopted TFA technique, it also involves high computational complexity, which makes the researchers to seek computationally-effective alternatives. The FSST was used in [36] as a substitute for CWD in the pre-preprocessing step, following which a multi-resolution CNN with three different kernel sizes was proposed. In [37], the WVD was adopted, and a VGG16 variant pretrained with ImageNet was used to reduce the training cost.…”

Section: A DL For Lpi Radarmentioning

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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“…Radar is widely deployed on the current battlefield and has progressively become the dominant key technology in modern warfare as a result of the continuous improvement of radar technology [1][2][3][4]. Therefore, recognizing the modulation type of enemy radar signals rapidly and accurately can effectively obtain battlefield information and situation and provide decent support for subsequent decision-making.…”

Section: Introductionmentioning

confidence: 99%

Radar Signal Recognition and Localization Based on Multiscale Lightweight Attention Model

Luo

Deng

2022

Journal of Sensors

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The recognition technology of the radar signal modulation mode plays a critical role in electronic warfare, and the algorithm based on deep learning has significantly improved the recognition accuracy of radar signals. However, the convolutional neural networks became increasingly sophisticated with the progress of deep learning, making them unsuitable for platforms with limited computing resources. ResXNet, a novel multiscale lightweight attention model, is proposed in this paper. The proposed ResXNet model has a larger receptive field and a novel grouped residual structure to improve the feature representation capacity of the model. In addition, the convolution block attention module (CBAM) is utilized to effectively aggregate channel and spatial information, enabling the convolutional neural network model to extract features more effectively. The input time-frequency image size of the proposed model is increased to 600 × 600 , which effectively reduces the information loss of the input data. The average recognition accuracy of the proposed model achieves 91.1% at -8 dB. Furthermore, the proposed model performs better in terms of unsupervised object localization with the class activation map (CAM). The classification information and localization information of the radar signal can be fused for subsequent analysis.

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LPI Radar Waveform Recognition Based on Multi-Resolution Deep Feature Fusion

Cited by 18 publications

References 22 publications

LPI Radar Waveform Recognition Based on Neural Architecture Search

LPI Radar Waveform Recognition Based on Neural Architecture Search

Deep-Learning for Radar: A Survey

Radar Signal Recognition and Localization Based on Multiscale Lightweight Attention Model

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