Paper
20 April 2023 A stacked auto-encoder and SVDD-based detection method for radar signal
Yuan Huang, Tao Liu, Jianbin Lu, Qianqian Liu
Author Affiliations +
Proceedings Volume 12602, International Conference on Electronic Information Engineering and Computer Science (EIECS 2022); 126020A (2023) https://doi.org/10.1117/12.2668492
Event: International Conference on Electronic Information Engineering and Computer Science (EIECS 2022), 2022, Changchun, China
Abstract
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.
© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Yuan Huang, Tao Liu, Jianbin Lu, and Qianqian Liu "A stacked auto-encoder and SVDD-based detection method for radar signal", Proc. SPIE 12602, International Conference on Electronic Information Engineering and Computer Science (EIECS 2022), 126020A (20 April 2023); https://doi.org/10.1117/12.2668492
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KEYWORDS
Signal detection

Radar

Education and training

Feature extraction

Signal to noise ratio

Detection and tracking algorithms

Deep learning

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