Multi-level radar signal open-set recognition based on SVM and K-means

In order to address the issue that traditional radar signal recognition techniques have trouble successfully identifying unknown modulated signals in practical situations, this paper suggests a multi-level radar signal open-set identification technique based on K-means clustering and Support Vector Machine (SVM) pre-training. After performing the multisynchro squeezing transform (MSST) on radar signals, the discrete wavelet transform (DWT) is employed to extract features from the preprocessed time-frequency images. An SVM classifier is trained using known radar signal data during the training phase. The classifier is used to distinguish between known and unknown modulation types during the testing phase in order to achieve open-set radar signal identification. Subsequently, K-means cluster analysis is applied to unknown radar signals, further classifying unknown modulation modes into different clusters, thereby expanding the recognition scope of radar signal modulation types. Experimental results demonstrate that the proposed method achieves a recognition accuracy of over 90% for both known and unknown signals at a signal-to-noise ratio (SNR) of −4 dB, effectively recognizing unknown modulation types.