Radar signal detection under low SNR using stacked auto-encoder and time-frequency domain features

Yuan Huang; Tao Liu; Ke Li

doi:10.1117/12.3032046

8 June 2024 Radar signal detection under low SNR using stacked auto-encoder and time-frequency domain features

Yuan Huang, Tao Liu, Ke Li

Proceedings Volume 13171, Third International Conference on Algorithms, Microchips, and Network Applications (AMNA 2024); 131711A (2024) https://doi.org/10.1117/12.3032046
Event: 3rd International Conference on Algorithms, Microchips and Network Applications (AMNA 2024), 2024, Jinan, China

Abstract

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yuan Huang, Tao Liu, and Ke Li "Radar signal detection under low SNR using stacked auto-encoder and time-frequency domain features", Proc. SPIE 13171, Third International Conference on Algorithms, Microchips, and Network Applications (AMNA 2024), 131711A (8 June 2024); https://doi.org/10.1117/12.3032046

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