Deep learning-based LPI radar signals analysis and identification using a Nyquist Folding Receiver architecture

Abstract Nyquist Folding Receiver (NYFR) is a perceptron structure that realizes a low probability of intercept (LPI) signal analog to information. Aiming at the problem of LPI radar signal receiving, the time domain, frequency domain, and time-frequency domain problems of signals intercepted by NYFR structure are studied. Combined with the time-frequency analysis (TFA) method, a radar recognition scheme based on deep learning (DL) is introduced, which can reliably classify common LPI radar signals. First, the structure of NYFR and its characteristics in the time domain, frequency domain, and time and frequency domain are analyzed. Then, the received signal is then converted into a time-frequency image (TFI). Finally, four kinds of DL algorithms are used to classify LPI radar signals. Simulation results demonstrate the correctness of the NYFR structure, and the effectiveness of the proposed recognition method is verified by comparison experiments.

[1]  Sumit Roy,et al.  Spectrum sharing between a surveillance radar and secondary Wi-Fi networks , 2016, IEEE Transactions on Aerospace and Electronic Systems.

[2]  Kelly Fitz,et al.  Algorithms for computing the time-corrected instantaneous frequency (reassigned) spectrogram, with applications. , 2004, The Journal of the Acoustical Society of America.

[3]  Yonina C. Eldar,et al.  Noise Folding in Compressed Sensing , 2011, IEEE Signal Processing Letters.

[4]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[5]  Jun Zhu,et al.  NYFR output pulse radar signal TOA analysis using extended Fourier transform and its TOA estimation , 2017 .

[6]  Shiban K. Koul,et al.  A Planar End-fire Array in S-band for Airborne Applications , 2012 .

[7]  V. Koivunen,et al.  Automatic Radar Waveform Recognition , 2007, IEEE Journal of Selected Topics in Signal Processing.

[8]  K. D. Rao,et al.  Automatic Intrapulse Modulation Classification of Advanced LPI Radar Waveforms , 2017, IEEE Transactions on Aerospace and Electronic Systems.

[9]  Jun Zhu,et al.  PARAMETER ESTIMATION OF LFM SIGNAL INTER- CEPTED BY SYNCHRONOUS NYQUIST FOLDING RE- CEIVER , 2011 .

[10]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[11]  Bryan Paul,et al.  Survey of RF Communications and Sensing Convergence Research , 2017, IEEE Access.

[12]  Phillip E. Pace,et al.  Analog-to-Information and the Nyquist Folding Receiver , 2012, IEEE Journal on Emerging and Selected Topics in Circuits and Systems.

[13]  Woongsup Lee,et al.  Deep Cooperative Sensing: Cooperative Spectrum Sensing Based on Convolutional Neural Networks , 2019, IEEE Transactions on Vehicular Technology.

[14]  S D Blunt,et al.  Intrapulse Radar-Embedded Communications , 2010, IEEE Transactions on Aerospace and Electronic Systems.

[15]  Bin Tang,et al.  Automatic LPI Radar Signal Sensing Method Using Visibility Graphs , 2020, IEEE Access.

[16]  Shauna Revay,et al.  Deep Learning for RF Device Fingerprinting in Cognitive Communication Networks , 2018, IEEE Journal of Selected Topics in Signal Processing.

[17]  Xiaodong Zeng,et al.  Nyquist Folding Receiver for the Interception of Frequency Agile Radar Signal , 2012 .

[18]  Kathleen M. Kaplan.  DoD electromagnetic spectrum strategy , 2015, 2015 IEEE Radar Conference (RadarCon).

[19]  Imran Ali Jokhio,et al.  Delta-MFCC Features and Information Theoretic Expectation Maximization based Text-independent Speaker Verification System , 2012 .

[20]  Kun Li,et al.  First Demonstration of Joint Wireless Communication and High-Resolution SAR Imaging Using Airborne MIMO Radar System , 2019, IEEE Transactions on Geoscience and Remote Sensing.

[21]  Yonina C. Eldar,et al.  Joint Radar-Communication Strategies for Autonomous Vehicles: Combining Two Key Automotive Technologies , 2020, IEEE Signal Processing Magazine.

[22]  Ming Zhang,et al.  LPI Radar Waveform Recognition Based on Time-Frequency Distribution , 2016, Sensors.

[23]  David L. Adamy,et al.  EW 102: A Second Course in Electronic Warfare , 2004 .

[24]  Yonina C. Eldar,et al.  Joint Radar-Communication Strategies for Autonomous Vehicles: Combining Two Key Automotive Technologies , 2019, IEEE Signal Processing Magazine.

[25]  Annamalai Annamalai,et al.  Application of Analytic Wavelet Transform for Signal Detection in Nyquist Folding Analog-to-Information Receiver , 2009, 2009 IEEE International Conference on Communications.

[26]  Nasser M. Nasrabadi,et al.  Pattern Recognition and Machine Learning , 2006, Technometrics.

[27]  Michael I. Jordan,et al.  Machine learning: Trends, perspectives, and prospects , 2015, Science.

[28]  Tamal Bose,et al.  Classification of LPI radar signals using spectral correlation and support vector machines , 2017 .

[29]  Richard G. Wiley,et al.  ELINT: The Interception and Analysis of Radar Signals , 2006 .

[30]  Biing-Hwang Juang,et al.  Deep Learning-Based End-to-End Wireless Communication Systems With Conditional GANs as Unknown Channels , 2019, IEEE Transactions on Wireless Communications.

[31]  J. Haupt,et al.  A Nyquist folding analog-to-information receiver , 2008, 2008 42nd Asilomar Conference on Signals, Systems and Computers.

[32]  Fredrik Tufvesson,et al.  5G mmWave Positioning for Vehicular Networks , 2017, IEEE Wireless Communications.

[33]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[34]  Bin Tang,et al.  A parameter estimation algorithm for LFM/BPSK hybrid modulated signal intercepted by Nyquist folding receiver , 2016, EURASIP J. Adv. Signal Process..

[35]  Zengping Chen,et al.  Parameter estimation of LFM signal intercepted by improved dual-channel Nyquist folding receiver , 2018 .

[36]  Seung-Hyun Kong,et al.  Automatic Recognition of General LPI Radar Waveform Using SSD and Supplementary Classifier , 2019, IEEE Transactions on Signal Processing.

[37]  Yoshua Bengio,et al.  Convolutional networks for images, speech, and time series , 1998 .

[38]  Zhiyu Qu,et al.  Radar Signal Intra-Pulse Modulation Recognition Based on Convolutional Neural Network , 2018, IEEE Access.

[39]  Won Namgoong,et al.  A channelized digital ultrawideband receiver , 2003, IEEE Trans. Wirel. Commun..

[40]  Phillip E. Pace,et al.  Detecting and Classifying Low Probability of Intercept Radar , 2009 .

[41]  Tao Li,et al.  Nyquist Zone Index and Chirp Rate Estimation of LFM Signal Intercepted by Nyquist Folding Receiver Based on Random Sample Consensus and Fractional Fourier Transform , 2019, Sensors.