Intra-pulse Intentional Modulation Recognition of Radar Signals at Low SNR

2018 ◽  
Author(s):  
Tian Xi ◽  
Liu Yishan ◽  
Pan Xianyue ◽  
Chen Wentao
Keyword(s):  
Modulation Recognition ◽  
Low Snr ◽  
Radar Signals

scholarly journals Radar Signals Modulation Recognition Based on Bispectrum Feature Processing

2021 ◽  
Author(s):  
xinping mi ◽  
xihong Chen ◽  
yufei Cao ◽  
qiang liu ◽  
xincheng song
Keyword(s):  
Noise Suppression ◽  
Signal To Noise Ratio ◽  
Low Noise ◽  
Modulation Recognition ◽  
Algorithm Optimization ◽  
Noise Resistance ◽  
Suppression Effect ◽  
Low Snr ◽  
Bee Colony ◽  
Radar Signals

Abstract Modulation recognition of radar signals is an important part of modern electronic intelligence reconnaissance and electronic support systems. In this paper, to solve the problem of low recognition accuracy and low noise resistance of radar signals under low signal-to-noise ratio(SNR), a recognition method based on variational mode decomposition(VMD) and bispectrum feature extraction is proposed. Based on the feature that bispectrum can suppress Gaussian noise, the feasibility of signals modulation recognition under low SNR is analyzed and the noise item is introduced. Due to the interference of noise item, the noise suppression effect of bispectrum is worse under 0dB. An improved VMD algorithm based on artificial bee colony(ABC) algorithm optimization and envelope entropy evaluation is proposed to preprocess the signal to improve the SNR. Finally, we designed a convolution neural network(CNN) classifier to recognize signals of different modulation types. The simulation results show that this method has better noise resistance than traditional methods, and can effectively identify different types of signals under low SNR.

scholarly journals Estimating the Instantaneous Frequency of Linear and Nonlinear Frequency Modulated Radar Signals—A Comparative Study

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2840
Author(s):  
Hubert Milczarek ◽  
Czesław Leśnik ◽  
Igor Djurović ◽  
Adam Kawalec
Keyword(s):  
Instantaneous Frequency ◽  
Early Warning Systems ◽  
Vital Role ◽  
Estimation Methods ◽  
Radar Signal ◽  
Nonlinear Frequency ◽  
Modulation Recognition ◽  
Electronic Warfare ◽  
Time Frequency ◽  
Radar Signals

Automatic modulation recognition plays a vital role in electronic warfare. Modern electronic intelligence and electronic support measures systems are able to automatically distinguish the modulation type of an intercepted radar signal by means of real-time intra-pulse analysis. This extra information can facilitate deinterleaving process as well as be utilized in early warning systems or give better insight into the performance of hostile radars. Existing modulation recognition algorithms usually extract signal features from one of the rudimentary waveform characteristics, namely instantaneous frequency (IF). Currently, there are a small number of studies concerning IF estimation methods, specifically for radar signals, whereas estimator accuracy may adversely affect the performance of the whole classification process. In this paper, five popular methods of evaluating the IF–law of frequency modulated radar signals are compared. The considered algorithms incorporate the two most prevalent estimation techniques, i.e., phase finite differences and time-frequency representations. The novel approach based on the generalized quasi-maximum likelihood (QML) method is also proposed. The results of simulation experiments show that the proposed QML estimator is significantly more accurate than the other considered techniques. Furthermore, for the first time in the publicly available literature, multipath influence on IF estimates has been investigated.

The Algorithm of Modulation Classification Based on Eigenface and Support Vector Machine in Low SNR

2014 ◽  
Vol 701-702 ◽  
pp. 442-448
Author(s):  
Xiang Ke Guo ◽  
Rong Ke Liu ◽  
Chang Yun Liu ◽  
Shao Hua Yue
Keyword(s):  
Support Vector Machine ◽  
Signal To Noise Ratio ◽  
Spectral Feature ◽  
Support Vector ◽  
Modulation Classification ◽  
Modulation Recognition ◽  
Low Snr ◽  
Feature Extract ◽  
Modulation Signal ◽  
Novel Algorithms

To improve the accuracy and reliability of modulation recognition at low signal to noise ratio (SNR) and few knowledge of signal parameter, the novel method based on the cyclic spectral feature and support vector machine(SVM) is presented. In the process of novel algorithms, the cyclic spectral analysis is used to realize the feature extract of the modulated signals, and the Eigenface method is used to reduce the amount of spectral coherence feature. Then, a new scheme of classification based on support vector machine is presented to classify the modulation signal. The experiment shows that the modulation classification accuracy of presented method is significantly improved at low SNR environment.

scholarly journals A Radar Signal Recognition Approach via IIF-Net Deep Learning Models

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Ji Li ◽  
Huiqiang Zhang ◽  
Jianping Ou ◽  
Wei Wang
Keyword(s):  
Recognition Accuracy ◽  
Signal To Noise Ratio ◽  
Recognition Rate ◽  
Software Radio ◽  
Radar Signal ◽  
Signal Recognition ◽  
Electromagnetic Environment ◽  
Time Frequency ◽  
Low Snr ◽  
Radar Signals

In the increasingly complex electromagnetic environment of modern battlefields, how to quickly and accurately identify radar signals is a hotspot in the field of electronic countermeasures. In this paper, USRP N210, USRP-LW N210, and other general software radio peripherals are used to simulate the transmitting and receiving process of radar signals, and a total of 8 radar signals, namely, Barker, Frank, chaotic, P1, P2, P3, P4, and OFDM, are produced. The signal obtains time-frequency images (TFIs) through the Choi–Williams distribution function (CWD). According to the characteristics of the radar signal TFI, a global feature balance extraction module (GFBE) is designed. Then, a new IIF-Net convolutional neural network with fewer network parameters and less computation cost has been proposed. The signal-to-noise ratio (SNR) range is −10 to 6 dB in the experiments. The experiments show that when the SNR is higher than −2 dB, the signal recognition rate of IIF-Net is as high as 99.74%, and the signal recognition accuracy is still 92.36% when the SNR is −10 dB. Compared with other methods, IIF-Net has higher recognition rate and better robustness under low SNR.

scholarly journals Radar signals modulation recognition based on bispectrum feature processing

2021 ◽  
Vol 1971 (1) ◽  
pp. 012099
Author(s):  
Xinping Mi ◽  
Xihong Chen ◽  
Qiang Liu ◽  
Denghua Hu
Keyword(s):  
Modulation Recognition ◽  
Feature Processing ◽  
Radar Signals

scholarly journals Modulation Recognition of Radar Signals Based on Adaptive Singular Value Reconstruction and Deep Residual Learning

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 449
Author(s):  
Kuiyu Chen ◽  
Shuning Zhang ◽  
Lingzhi Zhu ◽  
Si Chen ◽  
Huichang Zhao
Keyword(s):  
Background Noise ◽  
Singular Value ◽  
Modulation Recognition ◽  
Time Frequency ◽  
Feature Extracting ◽  
Residual Learning ◽  
Complex Process ◽  
Radar Signals ◽  
Electronic Intelligence ◽  
Processing Techniques

Automatically recognizing the modulation of radar signals is a necessary survival technique in electronic intelligence systems. In order to avoid the complex process of the feature extracting and realize the intelligent modulation recognition of various radar signals under low signal-to-noise ratios (SNRs), this paper proposes a method based on intrapulse signatures of radar signals using adaptive singular value reconstruction (ASVR) and deep residual learning. Firstly, the time-frequency spectrums of radar signals under low SNRs are improved after ASVR denoising processing. Secondly, a series of image processing techniques, including binarizing and morphologic filtering, are applied to suppress the background noise in the time-frequency distribution images (TFDIs). Thirdly, the training process of the residual network is achieved using TFDIs, and classification under various conditions is realized using the new-trained network. Simulation results show that, for eight kinds of modulation signals, the proposed approach still achieves an overall probability of successful recognition of 94.1% when the SNR is only −8 dB. Outstanding performance proves the superiority and robustness of the proposed method.

scholarly journals Machine Learning for Modulation Classification of Radar Signals: A Survey

10.31645/17 ◽  
2019 ◽  
Author(s):  
Hitham Alshoubaki ◽  
Keyword(s):  
Neural Network ◽  
Modulation Classification ◽  
Computation Complexity ◽  
Modulation Recognition ◽  
Low Snr ◽  
The Neural Network ◽  
Military Applications ◽  
Automatic Modulation Recognition ◽  
Deep Learning Neural Network

Automatic modulation recognition of radar waveform is a major topic and has many military applications. This paper surveys the models and the techniques used in recognizing different modulation types of intercepted radar waveform. The literature shows the outstanding performance of deep learning neural network at low SNR values and in signal- overlapped environments as well. Additionally, using different mathematical and statistical algorithms demonstrated that utilized in features extraction of the data in order to feed them into the neural network improves the performance significantly. However, reducing computation complexity is in development too.

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