A Rapid Accurate Recognition System for Radar Emitter Signals

Radar signal recognition is an indispensable part of an electronic countermeasure system. In order to solve the problem that the current techniques have, which is a low recognition rate and a slow recognition speed for radar signals, a rapid accurate recognition system is proposed, especially for when multiple signals arrive at the receiver. The proposed system can recognize eight types of radar signals while separating signals: binary phase shift keying (BPSK), linear frequency modulation (LFM), Costas, Frank code, and P1–P4 codes. Regression variational mode decomposition (RVMD) is explored to separate the received signals, which saves time for parameter optimization of variational mode decomposition (VMD). Furthermore, signal separation and a noise removal technique based on VMD and the first component recognition technique based on a deep belief network (DBN) are proposed. In addition, in order to overcome the loss of the secondary component caused by signal separation, a fusion network is explored to increase the recognition rate of the secondary component in a short time. The simulation results show that the recognition system achieves an overall recognition rate of 99.5% and 94% at a signal-to-noise ratio (SNR) of 0 dB when receiving single signals and double signals, while spending 0.8 s and 2.23 s, respectively. The proposed system can also be used to recognize medical and mechanical signals.

[1]  Seung-Hyun Kong,et al.  Automatic LPI Radar Waveform Recognition Using CNN , 2018, IEEE Access.

[2]  Fabian J. Theis,et al.  Sparse component analysis and blind source separation of underdetermined mixtures , 2005, IEEE Transactions on Neural Networks.

[3]  Anindya Bijoy Das,et al.  Discrimination and classification of focal and non-focal EEG signals using entropy-based features in the EMD-DWT domain , 2016, Biomed. Signal Process. Control..

[4]  Ram Bilas Pachori,et al.  Cross-terms reduction in the Wigner-Ville distribution using tunable-Q wavelet transform , 2016, Signal Process..

[5]  Tara N. Sainath,et al.  Deep Convolutional Neural Networks for Large-scale Speech Tasks , 2015, Neural Networks.

[6]  In-So Kweon,et al.  Partial Sum Minimization of Singular Values in Robust PCA: Algorithm and Applications , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Bao-Liang Lu,et al.  Investigating Critical Frequency Bands and Channels for EEG-Based Emotion Recognition with Deep Neural Networks , 2015, IEEE Transactions on Autonomous Mental Development.

[8]  Srdjan Stankovic,et al.  From the STFT to the Wigner Distribution [Lecture Notes] , 2014, IEEE Signal Processing Magazine.

[9]  J. Grajal,et al.  Digital channelized receiver based on time-frequency analysis for signal interception , 2005, IEEE Transactions on Aerospace and Electronic Systems.

[10]  Miloš Daković,et al.  From the STFT to the Wigner Distribution , 2013 .

[11]  Ming Zhang,et al.  Neural Networks for Radar Waveform Recognition , 2017, Symmetry.

[12]  Yan Liu,et al.  Discriminative deep belief networks for visual data classification , 2011, Pattern Recognit..

[13]  Seungjin Choi Blind Source Separation and Independent Component Analysis : A Review , 2004 .

[14]  Jing Tian,et al.  Motor Bearing Fault Detection Using Spectral Kurtosis-Based Feature Extraction Coupled With K-Nearest Neighbor Distance Analysis , 2016, IEEE Transactions on Industrial Electronics.

[15]  Zhao Kang,et al.  Nonnegative Matrix Factorization with Integrated Graph and Feature Learning , 2017, ACM Trans. Intell. Syst. Technol..

[16]  Kewei Cai,et al.  Harmonic separation from grid voltage using ensemble empirical-mode decomposition and independent component analysis , 2017 .

[17]  Yoshua Bengio,et al.  Extracting and composing robust features with denoising autoencoders , 2008, ICML '08.

[18]  Shaogang Gong,et al.  Towards Open-World Person Re-Identification by One-Shot Group-Based Verification , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

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

[21]  Prasanna Kumar Mundodu Krishna,et al.  Single Channel speech separation based on empirical mode decomposition and Hilbert Transform , 2017, IET Signal Process..

[22]  Na Lu,et al.  A Deep Learning Scheme for Motor Imagery Classification based on Restricted Boltzmann Machines , 2017, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[23]  Andrew Gerald Stove,et al.  Low probability of intercept radar strategies , 2004 .

[24]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.

[25]  Dominique Zosso,et al.  Variational Mode Decomposition , 2014, IEEE Transactions on Signal Processing.

[26]  Ke Wang,et al.  Radar Emitter Recognition Based on SIFT Position and Scale Features , 2018, IEEE Transactions on Circuits and Systems II: Express Briefs.

[27]  B. V. Rao,et al.  Hardgrove grindability index prediction using support vector regression , 2009 .

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

[29]  Yu Xue,et al.  A self-adaptive artificial bee colony algorithm based on global best for global optimization , 2017, Soft Computing.

[30]  Xiuping Jia,et al.  Deep Feature Extraction and Classification of Hyperspectral Images Based on Convolutional Neural Networks , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[31]  Stefan Reimann Symmetry and Network Structure , 2004, Neural Processing Letters.

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

[33]  Jong-Wook Kim,et al.  Particle Swarm Optimization Algorithm With Intelligent Particle Number Control for Optimal Design of Electric Machines , 2018, IEEE Transactions on Industrial Electronics.

[34]  Lan-Da Van,et al.  Energy-Efficient FastICA Implementation for Biomedical Signal Separation , 2011, IEEE Transactions on Neural Networks.