Advanced Search
Volume 45 Issue 5
May  2023
Turn off MathJax
Article Contents
GUAN Jian, WU Xijie, DING Hao, LIU Ningbo, HUANG Yong, CAO Zheng, WEI Jiayu. Detection of Small Targets on Sea Surface Based on 3-D Concave Hull Learning Algorithm[J]. Journal of Electronics & Information Technology, 2023, 45(5): 1602-1610. doi: 10.11999/JEIT220448
Citation: GUAN Jian, WU Xijie, DING Hao, LIU Ningbo, HUANG Yong, CAO Zheng, WEI Jiayu. Detection of Small Targets on Sea Surface Based on 3-D Concave Hull Learning Algorithm[J]. Journal of Electronics & Information Technology, 2023, 45(5): 1602-1610. doi: 10.11999/JEIT220448

Detection of Small Targets on Sea Surface Based on 3-D Concave Hull Learning Algorithm

doi: 10.11999/JEIT220448
Funds:  The National Natural Science Foundation of China (61871391, 61871392, 62101583)
  • Received Date: 2022-04-14
  • Accepted Date: 2022-08-25
  • Rev Recd Date: 2022-08-25
  • Available Online: 2022-08-30
  • Publish Date: 2023-05-10
  • For radar maritime target detection method of feature class, the convex hull classification algorithm is usually used in existing three feature detectors to complete detection. It is found that the decision region generated by convex hull learning algorithm may not well reflect the distribution of sea clutter samples in feature space in actual application, which may cause a certain degree of performance loss. By contrast, the decision region generated by concave hull algorithm is dug from convex hull, which can fit the distribution of sea clutter samples better. Therefore, in this paper, the form of the decision region is transformed from convex hull to concave hull. On this basis, a small target detection method based on 3-D concave hull learning algorithm is proposed. However, the existing 3-D concave hull algorithm has the disadvantages of low efficiency and unable to realize constant false alarm detection. To solve this problem, this paper improves the algorithm by optimizing the selection method of digging point and adding a process named "external complement". Finally, the measured CSIR datasets and X-band experimental radar data verify that the performance of proposed detection methods is superior to existing detection methods when other parameters are the same. At the same time, the analysis of algorithm complexity proves the application potential of proposed method.
  • loading
  • [1]
    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/JR20114

    GUAN Jian. Summary of marine radar target characteristics[J]. Journal of Radars, 2020, 9(4): 674–683. doi: 10.12000/JR20114
    [2]
    关键, 伍僖杰, 丁昊, 等. 基于对角积分双谱的海面慢速小目标检测方法[J]. 电子与信息学报, 2022, 44(7): 2449–2460. doi: 10.11999/JEIT210408

    GUAN Jian, WU Xijie, DING Hao, et al. A method for detecting small slow targets in sea surface based on diagonal integrated bispectrum[J]. Journal of Electronics &Information Technology, 2022, 44(7): 2449–2460. doi: 10.11999/JEIT210408
    [3]
    时艳玲, 姚婷婷, 郭亚星. 基于图连通密度的海面漂浮小目标检测[J]. 电子与信息学报, 2021, 43(11): 3185–3192.

    SHI Yanling, YAO Tingting, and GUO Yaxing. Floating small target detection based on graph connected density in sea surface[J] Journal of Electronics & Information Technology, 2021, 43(11): 3185–3192.
    [4]
    SHUI Penglang, LI Dongchen, and XU Shuwen. Tri-feature-based detection of floating small targets in sea clutter[J]. IEEE Transactions on Aerospace and Electronic Systems, 2014, 50(2): 1416–1430. doi: 10.1109/TAES.2014.120657
    [5]
    SHI Sainan and SHUI Penglang. Sea-surface floating small target detection by one-class classifier in time-frequency feature space[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(11): 6395–6411. doi: 10.1109/TGRS.2018.2838260
    [6]
    BAI Xiaohui, XU Shuwen, GUO Zixun, et al. Sea surface floating target detection based on local-distance measurement[C]. The 2021 IEEE 6th International Conference on Signal and Image Processing, Nanjing, China, 2021: 118–122.
    [7]
    许述文, 茹宏涛. 基于标签传播算法的海面漂浮小目标检测方法[J]. 电子与信息学报, 2022, 44(6): 2119–2126. doi: 10.11999/JEIT210382

    XU Shuwen and RU Hongtao. Small target detection on sea surface based on label propagation algorithm[J]. Journal of Electronics &Information Technology, 2022, 44(6): 2119–2126. doi: 10.11999/JEIT210382
    [8]
    郭子薰, 水鹏朗, 白晓惠, 等. 海杂波中基于可控虚警K近邻的海面小目标检测[J]. 雷达学报, 2020, 9(4): 654–663. doi: 10.12000/JR20055

    GUO Zixun, SHUI Penglang, BAI Xiaohui, et al. Sea-surface small target detection based on K-NN with controlled false alarm rate in sea clutter[J]. Journal of Radars, 2020, 9(4): 654–663. doi: 10.12000/JR20055
    [9]
    GUO Zixun and SHUI Penglang. Anomaly based sea-surface small target detection using K-nearest neighbor classification[J]. IEEE Transactions on Aerospace and Electronic Systems, 2020, 56(6): 4947–4964. doi: 10.1109/TAES.2020.3011868
    [10]
    ASAEEDI S, DIDEHVAR F, and MOHADES A. α-Concave hull, a generalization of convex hull[J]. Theoretical Computer Science, 2017, 702: 48–59. doi: 10.1016/j.tcs.2017.08.014
    [11]
    LI Peng and NIGGEMANN O. Improving clustering based anomaly detection with concave hull: An application in fault diagnosis of wind turbines[C]. The 2016 IEEE 14th International Conference on Industrial Informatics, Poitiers, France, 2016: 463–466.
    [12]
    HARTIGAN J A. Estimation of a convex density contour in two dimensions[J]. Journal of the American Statistical Association, 1987, 82(397): 267–270. doi: 10.2307/2289162
    [13]
    HERSELMAN P L, BAKER C J, and DE WIND H J. An analysis of X-Band calibrated sea clutter and small boat reflectivity at medium-to-low grazing angles[J]. International Journal of Navigation and Observation, 2008, 2008: 347518. doi: 10.1155/2008/347518
    [14]
    HERSELMAN P L and BAKER C J. Analysis of calibrated sea clutter and boat reflectivity data at C- and X-band in South African coastal waters[C]. 2007 IET International Conference on Radar Systems, Edinburgh, UK, 2007: 1–5.
    [15]
    刘宁波, 丁昊, 黄勇, 等. X波段雷达对海探测试验与数据获取年度进展[J]. 雷达学报, 2021, 10(1): 173–182. doi: 10.12000/JR21011

    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
    [16]
    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/JR19089

    LIU 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
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(9)  / Tables(4)

    Article Metrics

    Article views (536) PDF downloads(134) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return