Deep residual learning in modulation recognition of radar signals using higher-order spectral distribution

Chen, Kuiyu; Zhu, Lingzhi; Chen, Si; Zhang, Shuning; Zhao, Huichang

doi:10.1016/j.measurement.2021.109945

Cited by 13 publications

(4 citation statements)

References 32 publications

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“…Research has shown that the distribution of clutter noise, caused by a large number of scattering points, and the thermal noise from the receiver system tend to follow a Gaussian distribution [28]. Moreover, studies [29,30] demonstrate that the higher-order spectrum, as an analytical tool for time series, effectively suppresses Gaussian noise while preserving the characteristics of signals.…”

Section: Bispectrum Estimationmentioning

confidence: 99%

Modulation Recognition of Low-SNR UAV Radar Signals Based on Bispectral Slices and GA-BP Neural Network

Liu

Song

Chen

et al. 2023

Drones

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In this paper, we address the challenge of low recognition rates in existing methods for radar signals from unmanned aerial vehicles (UAV) with low signal-to-noise ratios (SNRs). To overcome this challenge, we propose the utilization of the bispectral slice approach for accurate recognition of complex UAV radar signals. Our approach involves extracting the bispectral diagonal slice and the maximum bispectral amplitude horizontal slice from the bispectrum amplitude spectrum of the received UAV radar signal. These slices serve as the basis for subsequent identification by calculating characteristic parameters such as convexity, box dimension, and sparseness. To accomplish the recognition task, we employ a GA-BP neural network. The significant variations observed in the bispectral slices of different signals, along with their robustness against Gaussian noise, contribute to the high separability and stability of the extracted bispectral convexity, bispectral box dimension, and bispectral sparseness. Through simulations involving five radar signals, our proposed method demonstrates superior performance. Remarkably, even under challenging conditions with an SNR as low as −3 dB, the recognition accuracy for the five different radar signals exceeds 90%. Our research aims to enhance the understanding and application of modulation recognition techniques for UAV radar signals, particularly in scenarios with low SNRs.

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Section: Bispectrum Estimationmentioning

confidence: 99%

Modulation Recognition of Low-SNR UAV Radar Signals Based on Bispectral Slices and GA-BP Neural Network

Liu

Song

Chen

et al. 2023

Drones

Self Cite

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“…In [7], TFIs denoised by singular value decomposition are presented to a shrinking ResNet-50 [8], which enhances the recognition performance. [9], [10] For most existing research, original 1D radar signals have to be mapped into a 2D transform domain [11], [12]. And then, 2D CNN is employed to extract features from 2D transform-domain images.…”

Section: Introductionmentioning

confidence: 99%

EMRNet: Efficient Modulation Recognition Networks for Continuous-Wave Radar Signals

CHEN

ZHANG

Zhang

et al. 2023

IEICE Trans. Electron.

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Automatic modulation recognition(AMR) of radar signals is a currently active area, especially in electronic reconnaissance, where systems need to quickly identify the intercepted signal and formulate corresponding interference measures on computationally limited platforms. However, previous methods generally have high computational complexity and considerable network parameters, making the system unable to detect the signal timely in resource-constrained environments. This letter firstly proposes an efficient modulation recognition network(EMRNet) with tiny and low latency models to match the requirements for mobile reconnaissance equipments. One-dimensional residual depthwise separable convolutions block(1D-RDSB) with an adaptive size of receptive fields is developed in EMRNet to replace the traditional convolution block. With 1D-RDSB, EMRNet achieves a high classification accuracy and dramatically reduces computation cost and network paraments. The experiment results show that EMRNet can achieve higher precision than existing 2D-CNN methods, while the computational cost and parament amount of EMRNet are reduced by about 13.93× and 80.88×, respectively.

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“…In Ref. [10], a novel modulation recognition method based on the high‐order spectrums of radar signals was proposed and further revealed the excellent recognition performance of the proposed method over the other four methods. In Ref.…”

Section: Introductionmentioning

confidence: 99%

Research on modulation recognition method of multi‐component radar signals based on deep convolution neural network

Wan

Deng

2023

IET Radar Sonar & Navi

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Modulation recognition of single‐component radar signals has been widely studied in the fewer years. Meanwhile, as the electromagnetic environment in the actual battlefields is ever‐increasingly complicated, the demand for the modulation recognition of multi‐component radar signals is also increasing. However, the modulation recognition of single‐component radar signals performs poorly on multi‐component radar signals. To address this problem, a novel modulation recognition method of multi‐component radar signals based on the deep convolutional neural network with a convolutional block attention module (DCNN‐CBAM) is proposed, and the network architecture mainly includes mobile‐inverted bottleneck convolution (MBConv), Fused‐MBConv, and convolutional block attention module (CBAM). An adaptive optimization algorithm Adam with weighted gradient and dynamic bound is proposed for accelerating convergence speed and generalisation performance. The proposed method can not only recognise dual‐component and three‐component radar signals but also four‐component radar signals. The experimental results demonstrate that the recognition accuracies of all radar signals modulation types are more than 83.57% at −8 dB for dual‐component radar signals, 67.41% for three‐component radar signals, and 57.95% for four‐component radar signals, which verify the better recognition performance of DCNN‐CBAM for multi‐component radar signals. The recognition accuracy of the proposed DCNN‐CBAM for dual‐component radar signals can be up to 95.95% at −6 dB, which shows superior recognition performance over the residual method. This work provides an essential guidance for enhancing the recognition performance of multi‐component radar signals.

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Deep residual learning in modulation recognition of radar signals using higher-order spectral distribution

Cited by 13 publications

References 32 publications

Modulation Recognition of Low-SNR UAV Radar Signals Based on Bispectral Slices and GA-BP Neural Network

Modulation Recognition of Low-SNR UAV Radar Signals Based on Bispectral Slices and GA-BP Neural Network

EMRNet: Efficient Modulation Recognition Networks for Continuous-Wave Radar Signals

Research on modulation recognition method of multi‐component radar signals based on deep convolution neural network

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