Classification of radar signals modulation based on SVM using wavelet entropy and empirical mode decomposition entropy

Robust classification of radar signals under low signal-to-noise ratio (SNR) conditions is critical for target recognition, electronic warfare, and radar emitter identification. However, the performance of conventional methods deteriorates severely in noisy environments due to interference and clutter. This paper proposes an effective classification framework based on a Support Vector Machine (SVM) that exploits the joint discriminative power of wavelet entropy and empirical mode decomposition (EMD) entropy. These two entropy measures characterize the intrinsic complexity and time–frequency structure of radar signals corrupted by noise and are combined into a compact two-dimensional feature vector. Extensive experiments on three representative radar modulation types—pulse Doppler (PD), linear frequency modulation (LFM), and pseudo-code phase modulation (PCPM)—demonstrate the robustness of the proposed method over a wide SNR range from −10 dB to 10 dB. The proposed classifier achieves 100% accuracy when the SNR is above 0 dB, maintains 95% accuracy at −5 dB, and still attains 83% accuracy at −10 dB. In comparative tests, it further achieves 56.7% accuracy at −15 dB, outperforming or matching several state-of-the-art SVM-based and deep-learning-based approaches.