杂乱
计算机科学
恒虚警率
雷达
人工智能
卷积神经网络
深度学习
遥感
算法
模式识别(心理学)
电信
地质学
作者
Jingang Wang,Songbin Li
出处
期刊:IEEE Geoscience and Remote Sensing Letters
[Institute of Electrical and Electronics Engineers]
日期:2023-01-01
卷期号:20: 1-5
被引量:2
标识
DOI:10.1109/lgrs.2022.3230443
摘要
Radar-based maritime target detection plays an important role in ocean monitoring. Considering the practical application, pulse-compression radar is widely used in terms of civilian offshore surface target detection. The existence of sea clutter will greatly interfere the detection performance of pulse-compression radar. This leads to the low detection performance of traditional algorithms like constant false alarm rate (CFAR). Deep learning methods have made strides in many fields recently, such as natural language processing and speech recognition. Inspired by this idea, we propose a maritime radar target detection method in sea clutter based on convolution neural network (CNN) and dual-perspective attention (DPA). The proposed method first encodes the radar echo in high-dimensional space and then extracts the correlation features from the global and local perspectives through the attention mechanism. We deployed the X-band pulse-compression radar on the coast of Hainan, China, and collected a lot of measured data. Experimental results demonstrate that the detection performance of our method outperforms the traditional CFAR methods and the latest deep learning-based methods. In the measured dataset, our proposed method can reach a detection probability of 93.59% under a false alarm rate (FAR) of $1e-3$ , reaching the practical application level.
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