{"title":"基于CNN双视角注意的海杂波海事雷达目标检测","authors":"Jingang Wang, Songbin Li","doi":"10.1109/LGRS.2022.3230443","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":13046,"journal":{"name":"IEEE Geoscience and Remote Sensing Letters","volume":"20 1","pages":"1-5"},"PeriodicalIF":4.0000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Maritime Radar Target Detection in Sea Clutter Based on CNN With Dual-Perspective Attention\",\"authors\":\"Jingang Wang, Songbin Li\",\"doi\":\"10.1109/LGRS.2022.3230443\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":13046,\"journal\":{\"name\":\"IEEE Geoscience and Remote Sensing Letters\",\"volume\":\"20 1\",\"pages\":\"1-5\"},\"PeriodicalIF\":4.0000,\"publicationDate\":\"2023-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Geoscience and Remote Sensing Letters\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1109/LGRS.2022.3230443\",\"RegionNum\":3,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Geoscience and Remote Sensing Letters","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1109/LGRS.2022.3230443","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Maritime Radar Target Detection in Sea Clutter Based on CNN With Dual-Perspective Attention
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.
期刊介绍:
IEEE Geoscience and Remote Sensing Letters (GRSL) is a monthly publication for short papers (maximum length 5 pages) addressing new ideas and formative concepts in remote sensing as well as important new and timely results and concepts. Papers should relate to the theory, concepts and techniques of science and engineering as applied to sensing the earth, oceans, atmosphere, and space, and the processing, interpretation, and dissemination of this information. The technical content of papers must be both new and significant. Experimental data must be complete and include sufficient description of experimental apparatus, methods, and relevant experimental conditions. GRSL encourages the incorporation of "extended objects" or "multimedia" such as animations to enhance the shorter papers.