基于DeepLabV3与GAN的雷达时频混叠多信号智能检测与分离

Intelligent Detection and Separation of Radar Time-Frequency Aliasing Multi-signal Based on DeepLabV3 and GAN

  • 摘要: 针对当前雷达信号智能检测研究中存在信号数量受限、样式单一、不便于后续处理等问题,本文提出了一种基于DeepLabV3与GAN的雷达时频混叠多信号智能检测与分离方法。首先对接收信号的时域数据通过时频变换得到二维的时频数据,利用DeepLabV3对多信号重叠的时频数据进行检测并且实现信号的分离,对于信号的重叠部分利用GAN对时域信号进行估计重构。实验结果表明,该方法在信噪比(SNR)为-3 dB时,检测平均交并比(mIoU)能够达到了85%以上,在SNR为6 dB时,分离后的信号与原信号相关系数高于0.85。

     

    Abstract: ‍ ‍In view of the current research on intelligent detection of radar signals, there are still some problems that have not been resolved,such as the limitation on the number of the signals, the rarity of modulation types and the inconvenience for subsequent processing, this paper proposes a radar time-frequency aliasing multi-signal intelligent detection and separation method based on DeepLabV3 and GAN. First, the time-domain data of the received signal is transformed to obtain the two-dimensional time-frequency data, and DeepLabV3 is used to detect the time-frequency data of multiple signals and realize the signal separation. For the overlapping part of the signal, this paper use GAN to perform the reconstruction of time-domain signal. Results of the experiment show that the mean intersection over union (mIoU) of the detection can reach more than 85% when the SNR is -3 dB, and the correlation coefficient between the separated signal and the original signal is higher than 0.85 when the SNR is 6 dB.

     

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