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Journals / Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering / 2020 / Cilt: 62 - Sayı: 2

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

Almıla BEKTAŞ
Halit ERGEZER
Pages
134–152
Publication Date
2020-12-31
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The cross-validated results obtained are compared with the best results in the literature.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Öz 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.
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Key Findings & Data:TFIs are generated using Choi-Williams Distribution.
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Conclusion & Impact:The cross-validated results obtained are compared with the best results in the literature.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Anahtar Kelimeler low probability of intercept radar · support vector machine · principal component analysis

Özet

Öz 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.

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Cite This Article

BEKTAŞ, A., ERGEZER, H. (2020). LPI RADAR WAVEFORM CLASSIFICATION USING BINARY SVM AND MULTI-CLASS SVM BASED ON PRINCIPAL COMPONENTS OF TFI. Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering, Cilt: 62 - Sayı: 2, 134–152. https://ajindex.com/articles/83724

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