scholarly journals Detection of Polyphase Codes Radar Signals in Low SNR

2016 ◽  
Vol 2016 ◽  
pp. 1-6 ◽  
Author(s):  
Runlan Tian ◽  
Guoyi Zhang ◽  
Rui Zhou ◽  
Wei Dong
Keyword(s):  
Hough Transform ◽  
Signal To Noise Ratio ◽  
Superior Performance ◽  
Radar Signal ◽  
Time Frequency ◽  
Low Snr ◽  
Radar Signals ◽  
Core Idea ◽  
Code Type ◽  
Electronic Intelligence

A novel effective detection method is proposed for electronic intelligence (ELINT) systems detecting polyphase codes radar signal in the low signal-to-noise ratio (SNR) scenario. The core idea of the proposed method is first to calculate the time-frequency distribution of polyphase codes radar signals via Wigner-Ville distribution (WVD); then the modified Hough transform (HT) is employed to cumulate all the energy of WVD’s ridges effectively to achieve signal detection. Compared with the generalised Wigner Hough transform (GWHT) method, the proposed method has a superior performance in low SNR and is not sensitive to the code type. Simulation results verify the validity of the proposed method.

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.

Separation of Intercepted Multi-Radar Signals Based on Parameterized Time-Frequency Analysis

Frequenz ◽  
2016 ◽  
Vol 70 (9-10) ◽  
Author(s):  
W. L. Lu ◽  
J. W. Xie ◽  
H. M. Wang ◽  
C. Sheng
Keyword(s):  
Frequency Analysis ◽  
Simulated Data ◽  
Radar Signal ◽  
Electronic Warfare ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Frequency Domains ◽  
Stationary Signals ◽  
Radar Signals ◽  
Electronic Intelligence

AbstractModern radars use complex waveforms to obtain high detection performance and low probabilities of interception and identification. Signals intercepted from multiple radars overlap considerably in both the time and frequency domains and are difficult to separate with primary time parameters. Time–frequency analysis (TFA), as a key signal-processing tool, can provide better insight into the signal than conventional methods. In particular, among the various types of TFA, parameterized time-frequency analysis (PTFA) has shown great potential to investigate the time–frequency features of such non-stationary signals. In this paper, we propose a procedure for PTFA to separate overlapped radar signals; it includes five steps: initiation, parameterized time-frequency analysis, demodulating the signal of interest, adaptive filtering and recovering the signal. The effectiveness of the method was verified with simulated data and an intercepted radar signal received in a microwave laboratory. The results show that the proposed method has good performance and has potential in electronic reconnaissance applications, such as electronic intelligence, electronic warfare support measures, and radar warning.

scholarly journals Detection and Parameter Extraction of Low Probability of Intercept Radar Signals using the Hough Transform

2016 ◽  
pp. 9-25
Author(s):  
Daniel L. Stevens ◽  
Stephanie A. Schuckers
Keyword(s):  
Hough Transform ◽  
Continuous Wave ◽  
Parameter Extraction ◽  
Time Frequency ◽  
Low Snr ◽  
Analysis Technique ◽  
Shift Keying ◽  
Low Probability ◽  
Low Probability Of Intercept ◽  
Radar Signals

Digital intercept receivers are currently moving away from Fourier-based analysis and towards classical time-frequency analysis techniques, such as the Wigner-Ville distribution, Choi-Williams distribution, spectrogram, and scalogram, for the purpose of analyzing low probability of intercept radar signals (e.g. triangular modulated frequency modulated continuous wave and frequency shift keying). Although these classical time-frequency techniques are an improvement over the Fourier-based analysis, they still suffer from a lack of readability, due to cross-term interference, and a mediocre performance in low SNR environments. This lack of readability may lead to inaccurate detection and parameter extraction of these radar signals. In this paper, the use of the Hough transform, because of its ability to suppresscross-term interference, separate signals from cross-terms, and perform well in the presence of noise, is proposed as an improved signal analysis technique. With these qualities, the Hough transform has the potential to produce better readability and consequently, more accurate signal detection and parameter extraction metrics.

scholarly journals Radar signal recognition based on triplet convolutional neural network

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Lutao Liu ◽  
Xinyu Li
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Signal To Noise Ratio ◽  
Poor Performance ◽  
Radar Signal ◽  
Time Frequency ◽  
Low Snr ◽  
Triplet Loss ◽  
Recognition Success ◽  
Fully Connected

AbstractRecently, due to the wide application of low probability of intercept (LPI) radar, lots of recognition approaches about LPI radar signal modulations have been proposed. However, facing the increasingly complex electromagnetic environment, most existing methods have poor performance to identify different modulation types in low signal-to-noise ratio (SNR). This paper proposes an automatic recognition method for different LPI radar signal modulations. Firstly, time-domain signals are converted to time-frequency images (TFIs) by smooth pseudo-Wigner–Ville distribution. Then, these TFIs are fed into a designed triplet convolutional neural network (TCNN) to obtain high-dimensional feature vectors. In essence, TCNN is a CNN network that triplet loss is adopted to optimize parameters of the network in the training process. The participation of triplet loss can ensure that the distance between samples in different classes is greater than that between samples with the same label, improving the discriminability of TCNN. Eventually, a fully connected neural network is employed as the classifier to recognize different modulation types. Simulation shows that the overall recognition success rate can achieve 94% at − 10 dB, which proves the proposed method has a strong discriminating capability for the recognition of different LPI radar signal modulations, even under low SNR.

scholarly journals Radar Signal Modulation Recognition Based on Sep-ResNet

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7474
Author(s):  
Yongjiang Mao ◽  
Wenjuan Ren ◽  
Zhanpeng Yang
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Signal To Noise Ratio ◽  
Morphological Processing ◽  
Radar Signal ◽  
Modulation Recognition ◽  
Time Frequency ◽  
Low Snr ◽  
Modulated Signals ◽  
Intelligent Recognition

With the development of signal processing technology and the use of new radar systems, signal aliasing and electronic interference have occurred in space. The electromagnetic signals have become extremely complicated in their current applications in space, causing difficult problems in terms of accurately identifying radar-modulated signals in low signal-to-noise ratio (SNR) environments. To address this problem, in this paper, we propose an intelligent recognition method that combines time–frequency (T–F) analysis and a deep neural network to identify radar modulation signals. The T–F analysis of the complex Morlet wavelet transform (CMWT) method is used to extract the characteristics of signals and obtain the T–F images. Adaptive filtering and morphological processing are used in T–F image enhancement to reduce the interference of noise on signal characteristics. A deep neural network with the channel-separable ResNet (Sep-ResNet) is used to classify enhanced T–F images. The proposed method completes high-accuracy intelligent recognition of radar-modulated signals in a low-SNR environment. When the SNR is −10 dB, the probability of successful recognition (PSR) is 93.44%.

scholarly journals LPI Radar Waveform Recognition Based on Features from Multiple Images

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 526 ◽  
Author(s):  
Zhiyuan Ma ◽  
Zhi Huang ◽  
Anni Lin ◽  
Guangming Huang
Keyword(s):  
Signal To Noise Ratio ◽  
Recognition Rate ◽  
Joint Decision ◽  
Time Frequency ◽  
Low Snr ◽  
Core Technology ◽  
Stationary Signals ◽  
Modulation Type ◽  
Radar Signals ◽  
Short Time

Detecting and classifying the modulation type of the intercepted noisy LPI (low probability of intercept) radar signals in real-time is a necessary survival technique in the electronic intelligence systems. Most radar signals have been designed to have LPI properties; therefore, the LPI radar waveform recognition technique (LWRT) has recently gained increasing attention. In this paper, we propose a multiple feature images joint decision (MFIJD) model with two different feature extraction structures that fully extract the pixel feature to obtain the pre-classification results of each feature image for the non-stationary characteristics of most LPI radar signals. The core technology of this model is combining the short-time autocorrelation feature image, double short-time autocorrelation feature image and the original signal time-frequency image (TFI) simultaneously input into the hybrid model classifier, which is suitable for non-stationary signals, and it has higher universality. We demonstrate the performance of MFIJD by simulating 11 types of the signals defined in this paper and generating training sets and test sets. The comparison with the literature shows that the proposed methods not only has a high universality for LPI radar signals, but also better adapts to LPI radar waveform recognition at low SNR (signal to noise ratio) environment. The overall recognition rate of the method reaches 87.7% when the SNR is −6 dB.

Adaptive Mean Ridgelet Transform Filtering for Detecting Signal and Comparison of Algorithm's Implements

2015 ◽  
Vol 719-720 ◽  
pp. 1171-1176
Author(s):  
Guang Ping Zhu ◽  
Hui Sun
Keyword(s):  
Frequency Domain ◽  
Hough Transform ◽  
Power Distribution ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Simulation Experiments ◽  
Ridgelet Transform ◽  
Time Frequency ◽  
Low Snr ◽  
Mean Filter

For solving the problem which the performance of detection was reduced in the low signal to noise ratio (SNR) using Wigner-Ville Hough transform (WHT), the method of XWVD adaptive mean Ridgelet transform filtering (XWVD-M-FRIT) was proposed. In this method, due to the power distribution of signal is different from noise or reverberation in time-frequency domain, so designed adaptive axial mean filter, then using Ridgelet transform filtering to restrain noise or reverberation. At last, it is to detect the signal using Hough transform. The results of real and simulation experiments showed, compared with WHT, in the low SNR the new method is feasible to restrain noise or reverberation in time-frequency domain for improving the performance of signal detection. furthermore, the performance of varying implement of adaptive mean and Ridgelet transform filtering were compared.

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

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Ji Li ◽  
Huiqiang Zhang ◽  
Jianping Ou ◽  
Wei Wang
Keyword(s):  
Deep Learning ◽  
Feature Fusion ◽  
Signal To Noise Ratio ◽  
Software Radio ◽  
Radar Signal ◽  
Time Frequency ◽  
Recognition Result ◽  
Electronic Countermeasure ◽  
Radar Signals ◽  
Deep Learning Model

In the field of electronic countermeasure, the recognition of radar signals is extremely important. This paper uses GNU Radio and Universal Software Radio Peripherals to generate 10 classes of close-to-real multipulse radar signals, namely, Barker, Chaotic, EQFM, Frank, FSK, LFM, LOFM, OFDM, P1, and P2. In order to obtain the time-frequency image (TFI) of the multipulse radar signal, the signal is Choi–Williams distribution (CWD) transformed. Aiming at the features of the multipulse radar signal TFI, we designed a distinguishing feature fusion extraction module (DFFE) and proposed a new HRF-Net deep learning model based on this module. The model has relatively few parameters and calculations. The experiments were carried out at the signal-to-noise ratio (SNR) of −14 ∼ 4 dB. In the case of −6 dB, the recognition result of HRF-Net reached 99.583% and the recognition result of the network still reached 97.500% under −14 dB. Compared with other methods, HRF-Nets have relatively better generalization and robustness.

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.

scholarly journals Radar Signal Emitter Recognition Based on Combined Ensemble Empirical Mode Decomposition and the Generalized S-Transform

2019 ◽  
Vol 2019 ◽  
pp. 1-15
Author(s):  
Bin Liu ◽  
Youqian Feng ◽  
Zhonghai Yin ◽  
Xiangyu Fan
Keyword(s):  
Empirical Mode Decomposition ◽  
Prior Information ◽  
Ensemble Empirical Mode Decomposition ◽  
Radar Signal ◽  
Time Frequency ◽  
Low Snr ◽  
Mode Decomposition ◽  
S Transform ◽  
Emitter Recognition ◽  
Generalized S Transform

Present radar signal emitter recognition approaches suffer from a dependency on prior information. Moreover, modern emitter recognition must meet the challenges associated with low probability of intercept technology and other obscuration methodologies based on complex signal modulation and must simultaneously provide a relatively strong ability for extracting weak signals under low SNR values. Therefore, the present article proposes an emitter recognition approach that combines ensemble empirical mode decomposition (EEMD) with the generalized S-transform (GST) for promoting enhanced recognition ability for radar signals with complex modulation under low signal-to-noise ratios in the absence of prior information. The results of Monte Carlo simulations conducted using various mixed signals with additive Gaussian white noise are reported. The results verify that EEMD suppresses the occurrence of mode mixing commonly observed using standard empirical mode decomposition. In addition, EEMD is shown to extract meaningful signal features even under low SNR values, which demonstrates its ability to suppress noise. Finally, EEMD-GST is demonstrated to provide an obviously better time-frequency focusing property than that of either the standard S-transform or the short-time Fourier transform.

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