High-frequency surface wave radar (HFSWR) is of great significance for maritime detection, but in the HFSWR echo signal, ship targets are often submerged in a variety of clutter and interference, making it difficult to detect vessels. In this paper, we propose an intelligent detection algorithm for targets concealed in strong clutter and complex interference environments. The algorithm has two stages: preprocessing and target detection. In the preprocessing stage, faster region-based convolutional neural networks Faster R-CNN are designed to identify and locate clutter and interference regions in the range Doppler spectrum; in the target detection stage, a two-level cascade algorithm is proposed. First, an extremum detection algorithm is proposed to identify suspicious target points in the clutter/interference regions, including real and false target points, to quickly obtain potential target positions. Second, in consideration of the characteristics of radar targets, two lightweight networks are designed to extract the CNN features and the stacked autoencoder features of the potential target locations. Then, fusion features are obtained and sent to an extreme learning machine that acts as a second-level classifier to distinguish between real and false target points. Experiments show that the proposed HFSWR target-detection algorithm has better performance for vessel detection in clutter/interference regions than the current mainstream detection algorithms.

中文翻译：

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基于 HFSWR 深度特征融合的杂波/干扰区域目标检测


高频表面波雷达（HFSWR）对于海上探测具有重要意义，但在HFSWR回波信号中，船舶目标往往淹没在各种杂波和干扰中，导致船舶探测困难。在本文中，我们提出了一种针对强杂波和复杂干扰环境中隐藏目标的智能检测算法。该算法有两个阶段：预处理和目标检测。在预处理阶段，设计了基于更快区域的卷积神经网络Faster R-CNN，用于识别和定位范围多普勒频谱中的杂波和干扰区域；在目标检测阶段，提出了两级级联算法。首先，提出一种极值检测算法来识别杂波/干扰区域中的可疑目标点，包括真实目标点和虚假目标点，以快速获得潜在目标位置。其次，考虑到雷达目标的特征，设计了两个轻量级网络来提取潜在目标位置的CNN特征和堆叠自动编码器特征。然后，获得融合特征并将其发送到极限学习机，该极限学习机充当二级分类器以区分真实和错误的目标点。实验表明，所提出的HFSWR目标检测算法对于杂波/干扰区域的船舶检测具有比当前主流检测算法更好的性能。