LPI RADAR WAVEFORM CLASSIFICATION USING BINARY SVM AND MULTI-CLASS SVM BASED ON PRINCIPAL COMPONENTS OF TFI

Year 2020, , 134 - 152, 31.12.2020

Almıla Bektaş , Halit Ergezer

https://doi.org/10.33769/aupse.690478

Abstract

Since cognition has become an important topic in Electronic Warfare (EW) systems, Electronic Support Measures (ESM) are used to monitor, intercept and analyse radar signals. Low Probability of Intercept (LPI) radars is preferred to be able to detect targets without being detected by ES systems. Because of their properties as low power, variable frequency, wide bandwidth, LPI Radar waveforms are difficult to intercept with ESM systems. In addition to intercepting, the determination of the waveform types used by the LPI Radars is also very important for applying counter-measures against these radars. In this study, a solution for the LPI Radar waveform recognition is proposed. The solution is based on the training of Support Vector Machine (SVM) after applying Principal Component Analysis (PCA) to the data obtained by Time-Frequency Images (TFI). TFIs are generated using Choi-Williams Distribution. High energy regions on these images are cropped automatically and then resized to obtain uniform data set. To obtain the best result in SVM, the SVM Hyper-Parameters are also optimized. Results are obtained by using one-against-all and one-against-one methods. Better classification performance than those given in the literature have been obtained especially for lower Signal to Noise Ratio (SNR) values. The cross-validated results obtained are compared with the best results in the literature.

Keywords

low probability of intercept radar, support vector machine, principal component analysis

References

• Pace, P.E., Detecting and Classifying Low Probability of Intercept Radar. 2nd Ed., Artech House, Norwood, MA, USA, 2009.
• Tao, R., Li, B., Sun, H., Research Progress of the Algebraic and Geometric Signal Processing, Defence Technology, 9(1) (2013), 40-47.
• Kong, S.H., Kim, M., Hoang, L.M., Kim, E., Automatic LPI Radar Waveform Recognition Using CNN, IEEE Access, 6 (2018), 4207-4219.
• Hoang, L.M., Kim, M., Kong, S.H., Automatic Recognition of General LPI Radar Waveform Using SSD and Supplementary Classifier, IEEE Transactions on Signal Processing, 67(13) 2019, 3516-3530.
• Gao, L., Zhang, X., Gao, J., You, S., Fusion Image Based Radar Signal Feature Extraction and Modulation Recognition, Access IEEE, 7 (2019), 13135-13148.
• Huang, Z., Ma, Z., Huang, G., Radar Waveform Recognition Based on Multiple Autocorrelation Images, Access IEEE, 7 (2019), 98653-98668.
• Gulum, T., Autonomous Non-Linear Classification of LPI Radar Signal Modulations, https://calhoun.nps.edu/handle/10945/3302; 2007 [accessed 24 September 2019].
• Kishore, T.R., Rao, K.D., Automatic intrapulse modulation classification of advanced LPI radar waveforms, IEEE Trans. Aerosp. Electron. Syst., 53(2) (2017), 901-914.
• Tong, X., Modelling and realization of real time electronic countermeasure simulation system based on SystemVue, Defence Technology, 2019.
• Deng, B., Luan, J., Cui, S., Analysis of parameter estimation using the sampling-type algorithm of discrete fractional Fourier transform, Defence Technology, 10(4) (2014), 321-327.
• Liu, M., Liao, G., Yang, Z., Song, H., Gong, F., Electromagnetic Signal Classification Based on Deep Sparse Capsule Networks, Access IEEE, 7 (2019), 83974-83983.
• Zeng, X., Wang, S., Bark-wavelet Analysis and Hilbert–Huang Transform for Underwater Target Recognition, Defence Technology, 9(2) (2013), 115-120.
• Choi, H.I., Williams, W.J., Improved time-frequency representation of multicomponent signals using exponential kernels, IEEE Trans. Acoust., Speech, Signal Process., 37(6) (1989), 862-871.
• Hollinger, K.B., Code optimization for the Choi-Williams distribution for ELINT applications, https://calhoun.nps.edu/handle/10945/4422; 2009 [accessed 24 September 2019].
• Liu, Y., Xiao, P., Wu, H., Xiao, W., LPI radar signal detection based on radial integration of Choi-Williams time-frequency image, Journal of Systems Engineering and Electronics, (2015), 973-981.
• Hastie, T., Tibshirani, R., Friedman, J., The Elements of Statistical Learning, 2nd Ed., Springer New York Inc, New York, NY, USA, 2001.
• Theodoridis, S., Koutroumbas, K., Pattern Recognition, 4th Ed., Academic Press. Inc., Orlando, FL, USA, 2009.
• Snoek, J., Larochelle, H., Adams, R.P., Practical Bayesian optimization of machine learning algorithms, Advances in Neural Information Processing Systems, 25 (2012), 2960-2968.
• Feurer, M., Hutter, F., Hyperparameter Optimization. In: Hutter F, Kotthoff L, Vanschoren, J., editors, Automated Machine Learning, Cham: Springer International Publishing, (2019), 3-33.
• Duda, R.O., Hart, P.E., Stork, D.G., Pattern Classification, 2nd Ed., John Wiley & Sons, Inc. New York, 2001.
• Powers, D.M.W., Evaluation: From Precision, Recall and F-Measure to Roc, Informedness, Markedness & Correlation, Journal of Machine Learning Technologies, 2(1) (2011), 37-63.
• Lima, A.F., Analysis of low probability of intercept (LPI) radar signals using cyclostationary processing, https://calhoun.nps.edu/handle/10945/4944, 2002 [accessed 24 December 2019].
• Platt, J. C., Cristianini, N., Shawe-Taylor, J., Large margin DAGs for multiclass classification, Advances in Neural Information Processing Systems, MIT Press,12 (2000), 547–553.