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

Authors : Almıla BEKTAŞ, Halit ERGEZER

Pages : 134-152

Doi:10.33769/aupse.690478

View : 8 | Download : 8

Publication Date : 2020-12-31

Article Type : Research Paper

Abstract :Since cognition has become an important topic in Electronic Warfare insert ignore into journalissuearticles values(EW); systems, Electronic Support Measures insert ignore into journalissuearticles values(ESM); are used to monitor, intercept and analyse radar signals. Low Probability of Intercept insert ignore into journalissuearticles values(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 insert ignore into journalissuearticles values(SVM); after applying Principal Component Analysis insert ignore into journalissuearticles values(PCA); to the data obtained by Time-Frequency Images insert ignore into journalissuearticles values(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 insert ignore into journalissuearticles values(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