对海雷达多维图像特征融合智能检测方法

海面目标检测是雷达信号处理中的重要内容，在军事、民用领域内都有重要应用价值。在海面目标雷达信号处理中，海杂波的存在对检测算法的性能有很大影响，传统的雷达信号处理方法多基于统计理论，对于复杂环境条件和多样的目标特性检测性能下降明显。近年来深度学习技术发展迅速，为可靠的海面目标的检测方法提供了技术支持。本文对近年来目标检测算法、深度学习方法的发展进行总结，从雷达信号数据结构和维度出发，采用深度学习理论，分别提出了基于二维图像、三维视频雷达信号、多维雷达信号多通道融合的智能处理框架，并以导航雷达图像海上目标智能检测为例，提出一种Precise ROI?Faster R?CNN雷达图像检测算法，通过构建的导航雷达数据集训练和测试，相比经典恒虚警检测和Faster R?CNN检测方法有更高的检测精确度和更好的泛化能力，从而为对海雷达智能导航和目标检测提供了有效的技术途径。

Detection method of multi-dimensional images feature intelligent fusion for marine radar

The detection of marine targets is an important part of radar signal processing, and has important application value in military and civilian fields. For the radar signal processing of marine targets, the presence of sea clutter has a great influence on the performance of the detection algorithm. Traditional radar signal processing methods are mostly based on statistical theory, and the detection performance under complex environmental conditions and diverse target characteristics may decrease significantly. In recent years, deep learning technology has developed rapidly, providing technical support for reliable target detection methods. This paper summarizes the development of target detection algorithms and deep learning methods in recent years. Starting from the data structure and dimensions of radar signals, using deep learning theory, the paper proposes a multi?channel fusion flowchart based on two?dimensional images, three?dimensional frames processing, and multi?dimension information fusion intelligent processing framework. Taking the detection of marine targets in navigation radar images as an example, a Precise ROI?Faster R?CNN radar image detection algorithm is proposed. It is compared with the classic method of target detection through the training and testing of the constructed navigation radar dataset. It is indicated that the proposed method is an effective technical approach with higher detection accuracy and better generalization ability compared with Classical False Alarm Rate(CFAR) and Faster R?CNN methods. It can be an effective solution for intelligent navigation and target detection of marine radars.