Radar detector performance analysis using EM simulations of targets' RCS

How, Teo Meng; Lun, Yap Han

doi:10.1109/radar.2016.7485300

Cited by 3 publications

(1 citation statement)

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“…In traditional signal detection, many energy detection methods are developed to improve the detection probability [1]. In those methods, the energy is accumulated in time domain or frequency domain under the background of noise, and then compared with the stated detection threshold to identify the exiting of signal [2][3][4]. The methods can detect the signal without any prior information, however, the detection accuracy of the energy detection methods decrease significantly at low SNR.…”

Section: Introductionmentioning

confidence: 99%

A stacked auto-encoder and SVDD-based detection method for radar signal

Yuan

Liu

et al. 2023

International Conference on Electronic Information Engineering and Computer Science (EIECS 2022)

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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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