基于SET-CNN的紧凑型地波雷达弱目标检测方法

Weak Target Detection Method Based on SET-CNN for Compact HFSWR

紧凑型高频地波雷达发射功率较低且采用小孔径阵列,导致雷达回波中的弱目标增多,进而引起基于距离-多普勒谱的目标检测方法性能降低,目标探测能力减弱。为提高紧凑型地波雷达对弱目标的检测性能,本文提出一种基于同步提取变换-卷积神经网络(Synchroextracting Transform-Convolutional Neural Network,SET-CNN)的紧凑型地波雷达弱目标检测方法:首先在时频谱处理中,利用信噪比方法抑制信号中的海杂波,减少杂波时频脊线对目标检测的影响;然后基于SET时频谱构建时频脊线样本数据库,再通过卷积神经网络进行时频脊线分类,并基于分类结果的后处理完成船只目标检测。通过仿真和实测数据验证提出的目标检测方法,结果表明,本文提出的方法能够有效检测到弱目标,提高紧凑型地波雷达的目标检测性能。

Compact High-Frequency Surface Wave Radar(HFSWR)with low transmit power and small aperture array leads to an increase of weak targets in the radar echoes,which in turn degrades the performance of range-Doppler spectrum-based target detection method,and weakens the target detection capability.In order to improve the detection performance of compact HFSWR on weak target signals,this paper proposes a weak target detection method for HFSWR based on Synchroextracting Transform-Convolutional Neural Network(SET-CNN).Firstly,the Signal-to-Noise Ratio(SNR)method is used to suppress the sea clutter in the signal and reduce the influence of the clutter time-frequency ridges on the target detection.Then the time-frequency ridge sample database is conducted based on the SET time-frequency spectrum,and then the time-frequency ridges are classified through the CNN.Thereafter the vessel targets are detected by the post-processing of the classification results.The proposed target detection method is validated by simulation and measured data,and the results show that the method proposed can effectively detect weak targets and improve the up-to-target detection performance of compact high-frequency surface wave radar.