Deep Fusion for Radar Jamming Signal Classification Based on CNN

Shao, Guangqing; Chen, Yushi; Yan, Wei

doi:10.1109/access.2020.3004188

Cited by 55 publications

(15 citation statements)

References 27 publications

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“…The CNN approach directly relates to the picture used to create the relationship mapping feature [29]. CNN's ability to automatically learn discriminant and invariant features from data [30].…”

Section: Proposed Methodsmentioning

confidence: 99%

DeepSkin: Robust Skin Cancer Classification Using Convolutional Neural Network Algorithm

2022

ijicom

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Classification of skin cancer is a growing research topic with significant challenges in the image processing. Learning algorithms for classifying a kind of skin cancer have been presented in recent articles to accelerate the diagnosis process with a rapid and accurate diagnosis. However, effective detection of skin cancer requires extensive graphical data. Inspired by deep learning successful results in computer vision, A Convolutional Neural Network (CNN) is proposed in this study to build a skin cancer classification model. We conduct this experiment by collecting massive skin cancer datasets, conducting pre-processing, training models, and evaluating the performance. Based on the experiment result, the benign and malignant classification model can obtain a good accuracy with a slight loss. Therefore, the results obtained reached an accuracy of 54%.

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Section: Proposed Methodsmentioning

confidence: 99%

DeepSkin: Robust Skin Cancer Classification Using Convolutional Neural Network Algorithm

2022

ijicom

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“…In order to improve the recognition ACC and generalization ability of the network, the gap between the classes is increased and the gap within the classes is reduced so that the samples can achieve better clustering eect. Therefore, we propose an eective L F function combining L SLSCE function [21], [22] and L C function [23].…”

Section: B Fusion Loss Functionmentioning

confidence: 99%

LPI Radar Signals Modulation Recognition Based on ACDCA-ResNeXt

Wang

et al. 2023

IEEE Access

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For low probability of intercept (LPI) radar waveform identication accuracy (ACC) problem at low Signal-to-Noise Ratios (SNRs), an approach based on time-frequency analysis (TFA) and Asymmetric Dilated Convolution Coordinate Attention Residual networks (ACDCA-ResNeXt) is proposed to recognize twelve kinds of LPI radar signals automatically. First, we apply Choi-Williams distribution (CWD), which shows superior performance at low SNRs, to transforming radar signals into time-frequency images (TFI). Then, in order to obtain the high-quality TFIs, a series of image processing techniques, including 2D Wiener ltering, image cutting, and image resize, are used to remove the background noise and redundant frequency bands of the TFI and obtain a xed-size gray scale image containing main morphological features of the TFI. Finally, the TFIs are input into ACDCA-ResNeXt network that can extract and learn deep features to recognize radar waveforms. Furthermore, a fusion loss function, which is composed of a soft-label smoothed cross entropy loss function and a center loss function, improves the generalization capability performance of network and achieves a better clustering eect. Experimental results demonstrate that, for twelve kinds of LPI radar waveforms, the overall recognition ACC of the proposed approach achieves 97.94% when SNR is -8 dB. INDEX TERMS Radar waveform recognition, time-frequency analysis (TFA), asymmetric convolution (AC), dilated convolution, coordinate attention (CA) mechanism.

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“…Hence, it has the advantages of long detection range and high resolution. The time-domain expression of the LFM signal is as follows [36]…”

Section: A Linear Frequency Modulation (Lfm) Signalmentioning

confidence: 99%

Deep Neural Network-Based Interrupted Sampling Deceptive Jamming Countermeasure Method

Quan

Sha³

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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With the development of digital radio frequency memory technology, the main-lobe deception jamming represented by interrupted-sampling repeater jamming (ISRJ) poses a severe challenge to radar. Traditional antijamming methods usually need to estimate the jamming parameters and have the risk of losing target information. For the above problems, this article proposes a deep neural network-based ISRJ recognition and antijamming target detection method which consists of four serial steps. First, the proposed method obtains the time-frequency image set of radar echoes by short-time Fourier transform (STFT). Second, a youonly-look-once (YOLO) model is used to detect the jammed echoes, and the positioning result is automatically corrected to avoid losing the target information. Third, the anti-ISRJ target ranging and velocity measurement datasets are constructed according to the positioning result. Finally, an anti-ISRJ target detection model based on the convolution neural network (CNN) is designed to extract features along different dimensions and obtain the range and velocity of the real targets. Experiments on simulated and measured datasets show that the proposed method has better antijamming detection performance than the traditional method, and does not need to estimate the jamming parameters.

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Deep Fusion for Radar Jamming Signal Classification Based on CNN

Cited by 55 publications

References 27 publications

DeepSkin: Robust Skin Cancer Classification Using Convolutional Neural Network Algorithm

DeepSkin: Robust Skin Cancer Classification Using Convolutional Neural Network Algorithm

LPI Radar Signals Modulation Recognition Based on ACDCA-ResNeXt

Deep Neural Network-Based Interrupted Sampling Deceptive Jamming Countermeasure Method

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