Due to the influence of the complex marine environment, the marine target detection based on statistical theory is difficult to achieve high-performance. Moreover, due to various targets' motion characteristics, only using a single feature for detection is unreliable. In this paper, from the perspective of feature extraction and classification, marine target and sea clutter are classified by deep learning methods. To achieve the required false alarm rate, the dual-channel convolutional neural networks (DCCNN) and false-alarm-controllable classifier (FACC)-based marine target detection method is proposed. Firstly, the measured sea clutter and the target signal are preprocessed to obtain the time-Doppler spectrum and amplitude information. The Marine-DCCNN (MDCCNN) is then constructed for features extraction and fusion, and the feature vectors of the signals are obtained. The performance of different feature extraction models is tested and compared. Finally, the FACC is used as a detector to classify the feature vectors into two categories and the control of the false alarm rate is realized. The detection performances were verified by two popular public radar datasets, i.e., IPIX radar dataset (floating target) and CSIR dataset (maneuvering marine target). The results show that compared with single-channel CNN and histogram of oriented gradient support vector machine (Hog-SVM) classification, a combination of MDCCNN feature extraction model and softmax classifier can achieve higher performance and controllable false alarm rate. Moreover, HH polarization and mixed training datasets under different sea states can help improve detection performance.

中文翻译：

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基于 CNN 多特征融合的海洋目标虚警可控雷达检测


由于复杂海洋环境的影响，基于统计理论的海洋目标检测难以实现高性能。而且，由于目标的运动特性多种多样，仅使用单一特征进行检测是不可靠的。本文从特征提取和分类的角度出发，通过深度学习方法对海洋目标和海杂波进行分类。为了达到所需的误报率，提出了基于双通道卷积神经网络（DCCNN）和误报可控分类器（FACC）的海洋目标检测方法。首先对实测海杂波和目标信号进行预处理，获得时间多普勒频谱和幅度信息。然后构建Marine-DCCNN（MDCCNN）进行特征提取和融合，得到信号的特征向量。测试并比较了不同特征提取模型的性能。最后利用FACC作为检测器将特征向量分为两类，实现误报率的控制。检测性能通过两个流行的公共雷达数据集IPIX雷达数据集（浮动目标）和CSIR数据集（机动海洋目标）进行了验证。结果表明，与单通道CNN和定向梯度支持向量机（Hog-SVM）直方图分类相比，MDCCNN特征提取模型和softmax分类器的结合可以实现更高的性能和可控的误报率。此外，不同海况下的HH偏振和混合训练数据集有助于提高检测性能。