Research on Recognition Method in Mixture Scenarios of Ships and Floating Targets

DING Hao; LI Ao; CAO Zheng; LIU Ningbo; WANG Guoqing; SUN Dianxing

doi:10.11999/JEIT251119

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2026 >

DING Hao, LI Ao, CAO Zheng, LIU Ningbo, WANG Guoqing, SUN Dianxing. Research on Recognition Method in Mixture Scenarios of Ships and Floating Targets[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251119

Citation:

DING Hao, LI Ao, CAO Zheng, LIU Ningbo, WANG Guoqing, SUN Dianxing. Research on Recognition Method in Mixture Scenarios of Ships and Floating Targets[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT251119

Citation:

PDF( 3765 KB)

Research on Recognition Method in Mixture Scenarios of Ships and Floating Targets

doi: 10.11999/JEIT251119 cstr: 32379.14.JEIT251119

1.
Naval Aviation University, Yantai 264001, China
2.
College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China

Funds: The National Natural Science Foundation of China (62388102, 62101583)

Accepted Date: 2026-02-09
Rev Recd Date: 2026-02-09

Available Online: 2026-03-01

Abstract

Abstract

Objective In radar maritime target detection scenarios, when two or more targets are located within the same range cell, they form mixture echoes, such as echoes from both ship and floating targets. Existing target recognition methods exhibit notable limitations in such scenarios, mainly because they focus on Doppler channel with strongest energy in time–frequency domain. To address this issue, this paper proposes a target recognition method that jointly integrates mode reconstruction and time–frequency features. The objective is to distinguish individual target without prior knowledge of whether the received echoes contain mixture targets or not, avoiding reliance on high range resolution or multi-polarization information. Methods The core idea is to introduce Variational Mode Decomposition (VMD) to decompose radar echoes into multiple modal components, thereby achieving Doppler-channel separation. To address the spurious modes and the fragmented representation of a single target across multiple modes after decomposition, energy-constrained mode filtering method and spectral consistency based mode clustering method are proposed for effective mode selection and reconstruction. Based on the reconstructed signals, we then exploit the time–frequency differences between ships and floating targets in terms of micro-motion and complexity by extracting features from two perspectives, namely motion stability and disorder degree of energy distribution, which are short for VF and REDDC features, so as to enable accurate identification of individual target. Results and Discussions The experiments are conducted using X-band radar measured data under sea states 2～4 (Table 1 and Table 2). The results show that, the proposed method achieves an average recognition accuracy of 97.32% in mixture scenarios, significantly outperforming existing four-feature recognition method (Table 3) as well as state-of-the-art methods (Fig. 9). After investigating the impact of the frequency separation between different targets, it is found that when the time–frequency ridge space exceeds 70 Hz, the recognition accuracy reaches 97.93% (Fig. 11). This result also provides empirical support for selecting reasonable clustering threshold in mode reconstruction stage. When mixture scenarios turns to single target scenarios because of relative motion, the proposed method achieves an average recognition accuracy of 93.34%, which is 4.62% higher than that of existing four-feature method (88.72%) (Table 4). The analysis also indicates that, to ensure the expected recognition accuracy, the observation duration for feature extraction should be no less than 0.25 s (Fig. 12). Conclusions This paper investigates the recognition problems in maritime multi-target mixture scenarios. By introducing VMD, the constituent components of mixture echoes are separated. To address spurious modes and fragmented representation of target information across multiple modes, energy-constrained mode filtering method and spectrum consistency based mode clustering method are proposed. The VF and REDDC features are extracted from structure perspective and complexity perspective respectively. SVM classifier is then employed to complete target recognition. Performance analyses confirm that the proposed method can effectively identify each constituent target in mixture echoes while maintaining superior recognition performance in single target scenarios. Future work will focus on improving computational efficiency and real-time capability by optimizing the stopping criteria of VMD iterations, and on exploring the application boundaries of the method using measured data under higher sea states.
- maritime target recognition,
- mixture scenarios,
- VMD,
- time-frequency analysis

FullText(HTML)

References(29)

References

[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]	丁昊, 唐天宇, 曹政, 等. 基于多尺度排列熵特征的海上漂浮目标识别方法[J/OL]. 系统工程与电子技术, https://link.cnki.net/urlid/11.2422.TN.20250730.1543.004, 2025. DING Hao, TANG Tianyu, CAO Zheng, et al. Identification method of maritime floating targets based on multiscale permutation entropy features[J/OL]. Systems Engineering and Electronics, https://link.cnki.net/urlid/11.2422.TN.20250730.1543.004, 2025.
[3]	RU Hongtao, XU Shuwen, HE Qi, et al. Marine small floating target detection method based on fusion weight and graph dynamic attention mechanism[J]. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61: 5111511. doi: 10.1109/TGRS.2023.3332127.
[4]	.李思维. 基于微多普勒特性的低慢小目标识别方法研究[D]. [硕士论文], 电子科技大学, 2024. doi: 10.27005/d.cnki.gdzku.2024.000875. LI Siwei. Research on LSS target recognition based on micro-Doppler characteristics[D]. [Master dissertation], University of Electronic Science and Technology of China, 2024. doi: 10.27005/d.cnki.gdzku.2024.000875.
[5]	黄小红, 贺夏, 辛玉林, 等. 基于时频特征的低分辨雷达微动多目标分辨方法[J]. 电子与信息学报, 2010, 32(10): 2342–2347. doi: 10.3724/SP.J.1146.2009.0314. HUANG Xiaohong, HE Xia, XIN Yulin, et al. Resolving multiple targets with micro-motions based on time-frequency feature with low-resolution radar[J]. Journal of Electronics & Information Technology, 2010, 32(10): 2342–2347. doi: 10.3724/SP.J.1146.2009.0314.
[6]	CUI Kaibo, WANG Wei, CHEN Xi, et al. A kind of method of anti-corner reflector interference for millimeter wave high resolution radar system[C]. 2016 Progress in Electromagnetic Research Symposium (PIERS), Shanghai, China, 2016: 1900–1906. doi: 10.1109/PIERS.2016.7734824.
[7]	王罗胜斌, 吴国庆, 徐振海, 等. 雷达极化域变焦角反组合体对抗方法: 抗质心式干扰[J]. 电子学报, 2022, 50(12): 2957–2968. doi: 10.12263/DZXB.20221139. WANG Luoshengbin, WU Guoqing, XU Zhenhai, et al. Radar polarization modulation countermeasures for combined corner reflector: Anti centroid jamming[J]. Acta Electronica Sinica, 2022, 50(12): 2957–2968. doi: 10.12263/DZXB.20221139.
[8]	黄孟俊, 陈建军, 赵宏钟, 等. 海面角反射器干扰微多普勒建模与仿真[J]. 系统工程与电子技术, 2012, 34(9): 1781–1787. doi: 10.3969/j.issn.1001-506X.2012.09.06. HUANG Mengjun, CHEN Jianjun, ZHAO Hongzhong, et al. Micro-Doppler modeling and simulating of corner reflector in sea surface[J]. Systems Engineering and Electronics, 2012, 34(9): 1781–1787. doi: 10.3969/j.issn.1001-506X.2012.09.06.
[9]	苏宁远, 陈小龙, 关键, 等. 基于卷积神经网络的海上微动目标检测与分类方法[J]. 雷达学报, 2018, 7(5): 565–574. doi: 10.12000/JR18077. SU Ningyuan, CHEN Xiaolong, GUAN Jian, et al. Detection and classification of maritime target with micro-motion based on CNNs[J]. Journal of Radars, 2018, 7(5): 565–574. doi: 10.12000/JR18077.
[10]	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/JR20084. XU 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.
[11]	姜星宇, 刘宁波, 丁昊, 等. 雷达海杂波时频谱特性分析[J]. 海军航空工程学院学报, 2019, 34(5): 413–417. doi: 10.7682/j.issn.1673-1522.2019.05.003. JIANG Xingyu, LIU Ningbo, DING Hao, et al. Time-frequency spectrum analysis of radar sea clutter[J]. Journal of Naval Aviation University, 2019, 34(5): 413–417. doi: 10.7682/j.issn.1673-1522.2019.05.003.
[12]	王晓亮, 王聪生, 施宇翔, 等. 雷达低信噪比旋翼无人机与飞鸟分类方法[J/OL]. 北京航空航天大学学报, https://doi.org/10.13700/j.bh.1001-5965.2024.0585, 2025. WANG Xiaoliang, WANG Congsheng, SHI Yuxiang, et al. The classification method of multirotor drones and flying birds under low signal-to-noise ratio for radar[J/OL]. Journal of Beijing University of Aeronautics and Astronautics, https://doi.org/10.13700/j.bh.1001-5965.2024.0585, 2025.
[13]	程昊, 刘卫平, 赵婵娟, 等. 面向子带分解的非均匀抽取均衡算法[J]. 计算机测量与控制, 2024, 32(7): 260–266. doi: 10.16526/j.cnki.11-4762/tp.2024.07.038. CHENG Hao, LIU Weiping, ZHAO Chanjuan, et al. Non-uniform extraction equalization algorithm for sub-band decomposition[J]. Computer Measurement & Control, 2024, 32(7): 260–266. doi: 10.16526/j.cnki.11-4762/tp.2024.07.038.
[14]	尚书宏, 曹亮, 鲁伟. 基于小波变换特征提取的脱介筛故障诊断算法[J]. 中国安全科学学报, 2023, 33(S2): 233–237. doi: 10.16265/j.cnki.issn1003-3033.2023.S2.0020. SHANG Shuhong, CAO Liang, and LU Wei. A delamination-screen fault diagnosis algorithm based on wavelet-transform feature extraction[J]. China Safety Science Journal, 2023, 33(S2): 233–237. doi: 10.16265/j.cnki.issn1003-3033.2023.S2.0020. (查阅网上资料,未找到对应的英文翻译,请确认).
[15]	胡锡坤, 金添. 基于自适应小波尺度选择的生物雷达呼吸与心跳分离方法[J]. 雷达学报, 2016, 5(5): 462–469. doi: 10.12000/JR16103. HU Xikun and JIN Tian. Adaptive wavelet scale selection-based method for separating respiration and heartbeat in bio-radars[J]. Journal of Radars, 2016, 5(5): 462–469. doi: 10.12000/JR16103.
[16]	李中余, 桂亮, 海宇, 等. 基于变分模态分解与优选的超高分辨ISAR成像微多普勒抑制方法[J]. 雷达学报(中英文), 2024, 13(4): 852–865. doi: 10.12000/JR24043. LI Zhongyu, GUI Liang, HAI Yu, et al. Ultrahigh-resolution ISAR micro-Doppler suppression methodology based on variational mode decomposition and mode optimization[J]. Journal of Radars, 2024, 13(4): 852–865. doi: 10.12000/JR24043.
[17]	丁昊, 朱晨光, 刘宁波, 等. 高海况条件下海面漂浮小目标特征提取与分析[J]. 海军航空大学学报, 2023, 38(4): 301–312. doi: 10.7682/j.issn.2097-1427.2023.04.001. DING Hao, ZHU Chenguang, LIU Ningbo, et al. Feature extraction and analysis of small floating targets in high sea conditions[J]. Journal of Naval Aviation University, 2023, 38(4): 301–312. doi: 10.7682/j.issn.2097-1427.2023.04.001.
[18]	祝振宁, 钟志贤, 王广斌, 等. 滚动轴承故障特征提取的VMD-IMCKD参数优化方法[J]. 噪声与振动控制, 2025, 45(6): 147–153,168. doi: 10.3969/j.issn.1006-1355.2025.06.023. ZHU Zhenning, ZHONG Zhixian, WANG Guangbin, et al. Method of fault feature extraction for rolling bearings based on parameter optimization of VMD-IMCKD[J]. Noise and Vibration Control, 2025, 45(6): 147–153,168. doi: 10.3969/j.issn.1006-1355.2025.06.023.
[19]	李中余, 桂亮, 海宇, 等. 基于变分模态分解与优选的超高分辨ISAR成像微多普勒抑制方法[J]. 雷达学报(中英文), 2024, 13(4): 852–865. doi: 10.12000/JR24043. (查阅网上资料,本条文献与第16条文献重复,请确认). LI Zhongyu, GUI Liang, HAI Yu, et al. Ultrahigh-resolution ISAR micro-Doppler suppression methodology based on variational mode decomposition and mode optimization[J]. Journal of Radars, 2024, 13(4): 852–865. doi: 10.12000/JR24043.
[20]	DRAGOMIRETSKIY K and ZOSSO D. Variational mode decomposition[J]. IEEE Transactions on Signal Processing, 2014, 62(3): 531–544. doi: 10.1109/TSP.2013.2288675.
[21]	吕方方. 海面目标动态回波仿真与特性分析[D]. [硕士论文], 西安电子科技大学, 2019. doi: 10.27389/d.cnki.gxadu.2019.002034. LV Fangfang. Dynamic echo simulation and characteristic analysis of sea surface targets[D]. [Master dissertation], Xidian University, 2019. doi: 10.27389/d.cnki.gxadu.2019.002034.
[22]	LI Yuxing, JIANG Xinru, and WU Junxian. Rating entropy and its multivariate version[J]. Mechanical Systems and Signal Processing, 2025, 226: 112368. doi: 10.1016/j.ymssp.2025.112368.
[23]	彭海. 皮尔逊相关系数应用于医学信号相关度测量[J]. 电子世界, 2017(7): 163. doi: 10.19353/j.cnki.dzsj.2017.07.129. PENG Hai. Application of the Pearson correlation coefficient to correlation measurement of medical signals[J]. Electronics World, 2017(7): 163. doi: 10.19353/j.cnki.dzsj.2017.07.129. (查阅网上资料,未找到对应的英文翻译,请确认).
[24]	史丽佼, 李明华. 国铁企业工务系统线路通过总重对劳动生产率的影响因素研究[J]. 铁道经济研究, 2025(4): 88–96. doi: 10.20162/j.cnki.issn.1004-9746.2025.04.11. SHI Lijiao and LI Minghua. Research on the influencing factors of total line passing weight on labor productivity in track engineering sector of national railway enterprises[J]. Railway Economics Research, 2025(4): 88–96. doi: 10.20162/j.cnki.issn.1004-9746.2025.04.11.
[25]	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.
[26]	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/JR19006. DING 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.
[27]	赵越, 陈之纯, 纠博, 等. 一种基于时频分析的窄带雷达飞机目标分类特征提取方法[J]. 电子与信息学报, 2017, 39(9): 2225–2231. doi: 10.11999/JEIT161204. ZHAO Yue, CHEN Zhichun, JIU Bo, et al. Narrowband aircraft targets feature extraction and classification based on time-frequency analysis[J]. Journal of Electronics & Information Technology, 2017, 39(9): 2225–2231. doi: 10.11999/JEIT161204.
[28]	李郝亮, 陈思伟. 海面角反射体电磁散射特性与雷达鉴别研究进展与展望[J]. 雷达学报, 2023, 12(4): 738–761. doi: 10.12000/JR23100. LI Haoliang and CHEN Siwei. Electromagnetic scattering characteristics and radar identification of sea corner reflectors: Advances and prospects[J]. Journal of Radars, 2023, 12(4): 738–761. doi: 10.12000/JR23100.
[29]	YU Hengli, CAO Zheng, WANG Guoqing, et al. A classification method for marine surface floating small targets and ship targets[J]. IEEE Journal on Miniaturization for Air and Space Systems, 2024, 5(2): 94–99. doi: 10.1109/JMASS.2024.3372116.