To improve radar signal detection accuracy of traditional methods under low SNR, a detection method based on stacked auto-encoder (SAE) and time-frequency domain features is proposed. The time-domain features, frequency-domain features and joint time-frequency domain features of signal are extracted by SAE to obtain the representative features of radar signal. The extracted features are input into support vector data description (SVDD) for open-set judgment to distinguish radar signal from background signal. Simulation results show that the accuracy and robustness of object detection are improved and the performance of object detection algorithms in complex environments is improved by integrating time-domain features and frequency-domain features information from the target background into detection decisions. It has practical significance for improving the detection accuracy of radar signal detection under low SNR.
KEYWORDS: Signal detection, Radar, Education and training, Feature extraction, Signal to noise ratio, Detection and tracking algorithms, Deep learning, Interference (communication), Performance modeling, Computer simulations
Aiming at the difficulty of radar signal detection in low signal-to-noise ratio (SNR) condition with traditional methods, a stacked auto-encoder (SAE) and support vector data description (SVDD) based detection method is proposed. Firstly, the radar signal with noise is extracted by SAE to obtain the representative features. Secondly, the SVDD is trained with the extracted features to obtain a spherical discriminative boundary for classification offline. Finally, the trained SAE-SVDD used as the one-class classifier to detect the signal by minimizing both the reconstruction error and the hypersphere volume simultaneously in a real-time manner. Simulation results indicate that the proposed algorithm can extract and identify the radar signal under noise condition effectively with a good robustness. It has practical significance for improving the accuracy of radar signal detection under low SNR.
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