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| Citation: | LIU Ningbo, DING Hao, HUANG Yong, et al. Annual progress of the sea-detecting X-band radar and data acquisition program[J]. Journal of Radars, 2021, 10(1): 173–182. doi: 10.12000/JR21011
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Annual Progress of the Sea-detecting X-band Radar and Data Acquisition Program
doi: 10.12000/JR21011
More Information-
Abstract
There is an urgent need for radar-measured data to tackle key technologies of radar maritime target detection. The ‘‘Sea-detecting X-band Radar and Data Acquisition Program’’, proposed in 2019, aims to obtain data through radar experiments and share them publicly. In 2020, the program continued to advance and conducted several experiments in three aspects, namely, Radar Cross-Section (RCS) calibration of radar targets, detection of sea clutter and target under different sea conditions, as well as detection and tracking of maneuvering targets in sea. The measurement data of the stainless steel sphere calibrator at different distances in radar slow-scanning mode, sea clutter in radar staring mode in different directions, sea target in radar staring mode, and marine engine speedboat in radar scanning mode are obtained. In addition, wind and wave data, data from the Automatic Identification System (AIS) of targets, visible/infrared data, and other associated sensor data are synchronously obtained.-
Keywords:
- Radar,
- Sea-detecting experiment,
- Sea clutter,
- Target detection,
- Data sharing
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References
[1] 丁昊, 刘宁波, 董云龙, 等. 雷达海杂波测量试验回顾与展望[J]. 雷达学报, 2019, 8(3): 281–302. doi: 10.12000/JR19006DING Hao, LIU Ningbo, DONG Yunlong, et al. Overview and prospects of radar sea clutter measurement experiments[J]. Journal of Radars, 2019, 8(3): 281–302. doi: 10.12000/JR19006[2] 关键. 雷达海上目标特性综述[J]. 雷达学报, 2020, 9(4): 674–683. doi: 10.12000/JR20114GUAN Jian. Summary of marine radar target characteristics[J]. Journal of Radars, 2020, 9(4): 674–683. doi: 10.12000/JR20114[3] 许述文, 白晓惠, 郭子薰, 等. 海杂波背景下雷达目标特征检测方法的现状与展望[J]. 雷达学报, 2020, 9(4): 684–714. doi: 10.12000/JR20084XU Shuwen, BAI Xiaohui, GUO Zixun, et al. Status and prospects of feature-based detection methods for floating targets on the sea surface[J]. Journal of Radars, 2020, 9(4): 684–714. doi: 10.12000/JR20084[4] 贺丰收, 何友, 刘准钆, 等. 卷积神经网络在雷达自动目标识别中的研究进展[J]. 电子与信息学报, 2020, 42(1): 119–131. doi: 10.11999/JEIT180899HE Fengshou, HE You, LIU Zhunga, et al. Research and development on applications of convolutional neural networks of radar automatic target recognition[J]. Journal of Electronics &Information Technology, 2020, 42(1): 119–131. doi: 10.11999/JEIT180899[5] 陈小龙, 黄勇, 关键, 等. MIMO雷达微弱目标长时积累技术综述[J]. 信号处理, 2020, 36(12): 1947–1964. doi: 10.16798/j.issn.1003-0530.2020.12.001CHEN Xiaolong, HUANG Yong, GUAN Jian, et al. Review of long-time integration techniques for weak targets using MIMO radar[J]. Journal of Signal Processing, 2020, 36(12): 1947–1964. doi: 10.16798/j.issn.1003-0530.2020.12.001[6] 刘宁波, 董云龙, 王国庆, 等. X波段雷达对海探测试验与数据获取[J]. 雷达学报, 2019, 8(5): 656–667. doi: 10.12000/JR19089LIU Ningbo, DONG Yunlong, WANG Guoqing, et al. Sea-detecting X-band radar and data acquisition program[J]. Journal of Radars, 2019, 8(5): 656–667. doi: 10.12000/JR19089 -
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- Figure 1. Experimental site and schematic diagram of calibration body’s trajectory
- Figure 2.
- Figure 2. Experimental scenario
- Figure 3. Wave (effective wave height, direction, period, speed) and wind (direction, speed) information (The red punctuation marks correspond to the experimental period)
- Figure 4. Typical data examples
- Figure 5. Sea clutter and target detection experimental scenario
- Figure 6.
- Figure 6. Typical sea clutter and target echo data
- Figure 7. Typical radar echo data
- Figure 8. Wave (effective wave height, direction, period, speed) and wind (direction, speed) information (The red punctuation marks correspond to the experimental period)
- Figure 9. Experimental target
- Figure 10. Set trajectory
- Figure 1. Release address of sea-detecting radar data