基于多尺度排列熵特征的海上漂浮目标识别方法

doi:10.12305/j.issn.1001-506X.2026.06.10

Abstract

Abstract:

Current radar-based methods for maritime floating target identification rely heavily on dual-polarization data or long-term observation, which significantly restricts their applicability in real-world scenarios. Addressing this issue, based on the theory of sea target seakeeping, it is found through analysis that the ranking fluctuation and randomness of floating targets under wave disturbance exhibit specific patterns. Utilizing the multi-scale permutation entropy (MPE) method for feature extraction, the regular characteristics of high temporal disturbance frequency and diverse permutation patterns of floating targets can be clearly presented. Based on this mechanism, a method for identifying seaborne floating targets using multi-scale permutation entropy features is proposed. The proposed method incorporates parameter optimization for feature extraction, where the embedding dimension and coarse-grained scale are systematically adjusted using Bhattacharyya distance as a quantitative separability index. Experimental validation with sea state 2 and 4 data demonstrates the method’s superior performance, achieving an average accuracy of 96.1% that surpasses existing approaches. Notably, the method maintains high recognition accuracy under challenging conditions including short observation time, varying observation angles, and single-polarization operation, proving its exceptional robustness.

Key words: radar, floating target, multiscale permutation entropy (MPE), target identification

CLC Number:

TN 957.51

Hao DING, Tianyu TANG, Zheng CAO, Hengli YU, Yunlong DONG. Identification method of maritime floating targets based on multiscale permutation entropy features[J]. Systems Engineering and Electronics, 2026, 48(6): 1880-1892.

Figures/Tables 15

Fig.1

Fig.2

Fig.3

Fig.4

Fig.5

Fig.6

Table 1

Table 2

Fig.7

Table 3

Table 4

Fig.8

Fig.9

Fig.10

Table 5

References 41

1	关键. 雷达海上目标特性综述[J]. 雷达学报, 2020, 9 (4): 674- 683.
	GUAN J. Summary of marine radar target characteristics[J]. Journal of Radars, 2020, 9 (4): 674- 683.
2	郭立新, 魏仪文. 复杂动态海面与目标电磁散射及回波仿真研究现状与展望[J]. 雷达学报, 2023, 12 (1): 76- 109.
	GUO L X, WEI Y W. Status and prospects of electromagnetic scattering echoes simulation from complex dynamic sea surfaces and targets[J]. Journal of Radars, 2023, 12 (1): 76- 109.
3	董云龙, 罗霄, 丁昊, 等. 基于特征时序性的海杂波环境小目标检测方法[J]. 电子与信息学报, 2025, 47 (3): 707- 719.
	DONG Y L, LUO X, DING H, et al. A detection method of small target in sea clutter environment based on feature temporal sequence[J]. Journal of Electronics & Information Technology, 2025, 47 (3): 707- 719.
4	丁昊, 韦继丰, 董云龙, 等. 高海况下应用先验信息的海上小目标检测方法[J]. 系统工程与电子技术, 2026, 48 (1): 44- 55.
	DING H, WEI J F, DONG Y L, et al. A method for detecting small targets using prior information under high sea states[J]. Systems Engineering and Electronics, 2026, 48 (1): 44- 55.
5	关键, 姜星宇, 刘宁波, 等. 海杂波背景下的双极化最大特征值目标检测[J]. 系统工程与电子技术, 2024, 46 (11): 3715- 3725.
	GUAN J, JIANG X Y, LIU N B, et al. Dual-polarized maximum eigenvalue-based target detection in sea clutter environment[J]. Systems Engineering and Electronics, 2024, 46 (11): 3715- 3725.
6	关键, 姜星宇, 刘宁波, 等. 海杂波中目标分数域谱范数特征检测方法[J]. 电子与信息学报, 2023, 45 (6): 2162- 2170.
	GUAN J, JIANG X Y, LIU N B, et al. Spectral norm feature detection method in FRFT domain of targets in sea clutter[J]. Journal of Electronics & Information Technology, 2023, 45 (6): 2162- 2170.
7	GUO Z X, BAI X H, LI J Y, et al. Small target detection in sea clutter using dominant clutter tree based on anomaly detection framework[J]. Signal Processing, 2024, 219, 109399. doi: 10.1016/j.sigpro.2024.109399
8	SHUI P L, ZHANG L, BAI X. Small target detection in sea clutter by weighted biased soft-margin SVM algorithm in feature spaces[J]. IEEE Sensors Journal, 2024, 24 (7): 10419- 10433. doi: 10.1109/JSEN.2024.3350571
9	WU X J, DING H, LIU N B, et al. A method for detecting small targets in sea surface based on singular spectrum analysis[J]. IEEE Trans. on Geoscience and Remote Sensing, 2022, 60, 5110817.
10	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.
	GUAN J, WU X J, DING H, 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.
11	WU X J, DING H, LIU N B, et al. Priori information-based feature extraction method for small target detection in sea clutter[J]. IEEE Trans. on Geoscience and Remote Sensing, 2022, 60, 5115315.
12	董云龙, 张兆祥, 丁昊, 等. 基于三特征预测的海杂波中小目标检测方法[J]. 雷达学报, 2023, 12 (4): 762- 775. doi: 10.12000/JR23037
	DONG Y L, ZHANG Z X, DING H, et al. Target detection in sea clutter using a three-feature prediction-based method[J]. Journal of Radars, 2023, 12 (4): 762- 775. doi: 10.12000/JR23037
13	韩静雯, 杨勇, 连静, 等. 大掠射角下幅相极化特征融合雷达导引头舰船检测方法[J]. 系统工程与电子技术, 2025, 47 (4): 1127- 1135.
	HAN J W, YANG Y, LIAN J, et al. Ship target detection method at high grazing angle based on phase and amplitude polarization feature information fusion for radar seeker[J]. Systems Engineering and Electronics, 2025, 47 (4): 1127- 1135.
14	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 H L, CHEN S W. 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
15	张坤. 高分辨雷达舰船距离像估计和遮蔽小目标检测方法研究[D]. 西安: 西安电子科技大学, 2021.
	ZHANG K. Methods of ship HRRP estimation and masked small target detection in high resolution radars[D]. Xi’an: Xidian University, 2021.
16	吕方方. 海面目标动态回波仿真与特性分析[D]. 西安: 西安电子科技大学, 2019.
	LV F F. Dynamic echo simulation and characteristic analysis of sea surface targets[D]. Xi’an: Xidian University, 2019.
17	韩静雯, 杨勇, 连静, 等. 基于极化与距离像特征融合的雷达导引头角反射器鉴别方法[J]. 系统工程与电子技术, 2024, 46 (11): 3658- 3670.
	HAN J W, YANG Y, LIAN J, et al. Identification method of corner reflector based on polarization and HRRP feature fusion for radar seeker[J]. Systems Engineering and Electronics, 2024, 46 (11): 3658- 3670.
18	艾小锋, 邱梦奇, 胡旖航, 等. 宽带复合双基地雷达舰船目标长度估计方法[J]. 电子与信息学报, 2024, 46 (3): 944- 951. doi: 10.11999/JEIT230088
	AI X F, QIU M Q, HU Y H, et al. Instantaneous length estimation of ships through wideband composite bistatic radar[J]. Journal of Electronics & Information Technology, 2024, 46 (3): 944- 951. doi: 10.11999/JEIT230088
19	RU H T, XU S W, HE Q, et al. Marine small floating target detection method based on fusion weight and graph dynamic attention mechanism[J]. IEEE Trans. on Geoscience and Remote Sensing, 2023, 61, 5111511.
20	TAO Z Y, GUO Y, FU Q. Discrimination method of ship and corner reflector based on SVM[C]//Proc. of the 6th International Conference on Instrumentation & Measurement, Computer, Communication and Control, 2016: 699−702.
21	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.
	HUANG M J, CHEN J J, ZHAO H Z, et al. Micro-Doppler modeling and simulating of corner reflector in sea surface[J]. Journal of Systems Engineering and Electronics, 2012, 34 (9): 1781- 1787.
22	黄孟俊, 赵宏钟, 付强, 等. 一种基于微多普勒特征的海面角反射器干扰鉴别方法[J]. 宇航学报, 2012, 33 (10): 1486- 1491.
	HUANG M J, ZHAO H Z, FU Q, et al. A sea corner-reflector jamming identification method based on micro-Doppler feature[J]. Journal of Astronautics, 2012, 33 (10): 1486- 1491.
23	张群, 胡健, 罗迎, 等. 微动目标雷达特征提取、成像与识别研究进展[J]. 雷达学报, 2018, 7 (5): 531- 547.
	ZHANG Q, HU J, LUO Y, et al. Research progresses in radar feature extraction, imaging, and recognition of target with micro-motions[J]. Journal of Radars, 2018, 7 (5): 531- 547.
24	范学满, 胡生亮, 贺静波. 对海雷达目标识别中全极化HRRP的特征提取与选择[J]. 电子与信息学报, 2016, 38 (12): 3261- 3268.
	FAN X M, HU S L, HE J B. Feature extraction and selection of full polarization HRRP in target recognition process of maritime surveillance radar[J]. Journal of Electronics & Information Technology, 2016, 38 (12): 3261- 3268.
25	吴国庆, 王罗胜斌, 庞晨, 等. 雷达极化域变焦角反组合体对抗方法: 抗冲淡式干扰[J]. 电子学报, 2022, 50 (12): 2969- 2983. doi: 10.12263/DZXB.20220979
	WU G Q, WANG L S B, PANG C, et al. Radar polarization modulation countermeasures for combined corner reflector: anti diluted jamming[J]. Acta Electronica Sinica, 2022, 50 (12): 2969- 2983. doi: 10.12263/DZXB.20220979
26	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 L S B, WU G Q, XU Z H, 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
27	郑近德, 姚殷柔, 潘海洋, 等. 多尺度熵方法在机械故障诊断中的应用研究进展[J]. 安徽工业大学学报（自然科学版）, 2024, 41 (1): 46- 57. doi: 10.12415/j.issn.1671-7872.23170
	ZHENG J D, YAO Y R, PAN H Y, et al. Research progress on the application of multi-scale entropy method in the mechanical fault diagnosis[J]. Journal of Anhui University of Technology （Natural Science）, 2024, 41 (1): 46- 57. doi: 10.12415/j.issn.1671-7872.23170
28	YANG J W, GULRAIZ I C, SUSANTO R, et al. Classification of interbeat interval time-series using attention entropy[J]. IEEE Trans. on Affective Computing, 2023, 14 (1): 321- 330. doi: 10.1109/TAFFC.2020.3031004
29	AZIZ W, ARIF M. Multiscale permutation entropy of phsiological time series[C]//Proc. of the 9th International Multitopic Conference, 2005.
30	席旭刚, 汤敏彦, 张自豪, 等. 融合表面肌电和加速度信号的下肢运动模式识别研究[J]. 电子学报, 2017, 45 (11): 2735- 2741. doi: 10.3969/j.issn.0372-2112.2017.11.022
	XI X G, TANG M Y, ZHANG Z H, et al. Lower limb motion recognition based on the fusion of sEMG and acceleration signal[J]. Acta Electronica Sinica, 2017, 45 (11): 2735- 2741. doi: 10.3969/j.issn.0372-2112.2017.11.022
31	产秀秀. 机载雷达下海面目标检测方法研究[D]. 西安: 西安电子科技大学, 2021.
	CHAN X X. Research on sea surface target detection method under airborne radar[D]. Xi’an: Xidian University, 2021.
32	EUISUN C, CHULHEE L. Feature extraction based on the Bhattacharyya distance[C]//Proc. of the IEEE International Conference on Geoscience and Remote Sensing Symposium, 2000: 2146−2148.
33	BHATTACHARYYA A. On a measure of divergence between two statistical populations defined by their probability distributions[J]. Bulletin of the Calcutta Mathematical Society, 1943, 35 (1): 99- 109.
34	CHRYSOSTOMOU C, SEKER H. Structural classification of protein sequences based on signal processing and support vector machines[C]//Proc. of the 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2016: 3088−3091.
35	PANTELIS B, SERGIOS T, CHARALAMPOS M, et al. Complex support vector machines for regression and quaternary classification[J]. IEEE Trans. on Neural Networks and Learning Systems, 2015, 26 (6): 1260- 1274. doi: 10.1109/TNNLS.2014.2336679
36	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.
	DING H, ZHU C G, LIU N B, 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.
37	关键, 刘宁波, 王国庆, 等. 雷达对海探测试验与目标特性数据获取-海上目标双极化多海况散射特性数据集[J]. 雷达学报, 2023, 12 (2): 456- 469. doi: 10.12000/JR23029
	GUAN J, LIU N B, WANG G Q, et al. Sea-detecting radar experiment and target feature data acquisition for dual polarization multistate scattering dataset of marine targets[J]. Journal of Radars, 2023, 12 (2): 456- 469. doi: 10.12000/JR23029
38	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 N B, DING H, HUANG Y, 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
39	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 N B, DONG Y L, WANG G Q, et al. Sea-detecting X-band radar and data acquisition program[J]. Journal of Radars, 2019, 8 (5): 656- 667. doi: 10.12000/JR19089
40	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 Y, CHEN Z C, JIU B, 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
41	YU H L, CAO Z, WANG G Q, 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

雷达参数	参数设置
工作频率范围/GHz	9.3～9.5
量程/nm	6
带宽/MHz	25
距离分辨率/m	6
脉冲重复频率/kHz	2
发射峰值功率/W	100
天线长度/m	2
天线极化方式	HH、VV
天线水平波束宽度/（°）	1.2

数据序号	极化	海况	脉冲数	目标类型
1	HH	2级	131072	货船+航道浮标
2	VV	2级	131072	货船+航道浮标
3	HH	4级	131072	货船+航道浮标
4	VV	4级	131072	货船+航道浮标

数据序号	测试集样本数	TP	FN	FP	TN	准确率/%
1	128	62	2	1	63	97.66
2	128	61	4	11	52	88.28
3	128	55	8	8	57	87.50
4	128	62	2	6	58	93.75

货船姿态	测试集样本数	TP	FN	FP	TN	准确率/%
径向视角	128	62	3	5	58	93.75
横向视角	128	61	2	5	60	94.53

[1]	Weishan ZHAO, Siyang YOU, Lijia HUANG, Guangyao ZHOU. Dual-stream SAR aircraft detection network enhanced by scattering topology [J]. Systems Engineering and Electronics, 2026, 48(6): 1848-1858.
[2]	Yongkang YUAN, Junxu WANG, Yuming HUA, Lei ZHANG. Earth-based radar Lunar imaging using Doppler compensation method [J]. Systems Engineering and Electronics, 2026, 48(6): 1859-1868.
[3]	Yuquan LUO, Yuqiang HE, Yaxin LI, Song LIANG, Jun WANG. Millimeter-wave radar human pose estimation based on CNN-BiLSTM-MHA spatio-temporal fusion framework [J]. Systems Engineering and Electronics, 2026, 48(5): 1502-1514.
[4]	Xunming HU, Jianxin WU. Multi-beam jamming method for distributed radar anti-mainlobe jamming technology [J]. Systems Engineering and Electronics, 2026, 48(5): 1532-1538.
[5]	Xin WANG, Shenghua ZHOU, Xinxun ZHANG, Jingyi NAN, Shikun SUN. Full polarization MIMO radar mainlobe suppression jamming suppression method [J]. Systems Engineering and Electronics, 2026, 48(5): 1539-1550.
[6]	Wenbin LIN, Chenguang SHI, Mu YAN, Fei WANG, Jianjiang ZHOU. Robust trajectory planning algorithm for UAVs swarm facing deceptive jamming networked radar [J]. Systems Engineering and Electronics, 2026, 48(5): 1551-1563.
[7]	Zeying YANG, Zhuo CHEN, Zhiming XU, Xiaofeng AI, Qihua WU, Ling WANG. Forward labeling method for ISAR image components of space targets [J]. Systems Engineering and Electronics, 2026, 48(4): 1165-1173.
[8]	Tianyou HUANG, Huifu LIN, Chao YANG, Tao LAI, Qingsong WANG, Haifeng HUANG. A method for resolving range mainlobe broadening in SAR frequency-shift deception jamming [J]. Systems Engineering and Electronics, 2026, 48(4): 1174-1185.
[9]	Jijun HU, Wei HAN, Guoyu ZHANG, Yang LI, Jie YANG, Tian TIAN. SAR operating mode recognition method based on behavioral recognition network [J]. Systems Engineering and Electronics, 2026, 48(4): 1218-1229.
[10]	Wei WANG, Yong YANG, Jingwen HAN. Radar sea surface weak target detection technology based on correlation [J]. Systems Engineering and Electronics, 2026, 48(3): 787-794.
[11]	Jia ZHAI, Qijia HUA, Ziquan WANG, Zikai ZHANG, Ruiqi WANG, Jinling LIU, Meiqi HU, Chenhui WU. Joint recognition of visible light and SAR images based on multi-level fusion [J]. Systems Engineering and Electronics, 2026, 48(3): 817-825.
[12]	Chenran GAO, Xichuan ZHANG, Yuanyi XIONG, Wei CHEN, Wenchong XIE. Anti-mainlobe unintentional jamming method for UAV-borne early warning radar based on frequency-domain filtering [J]. Systems Engineering and Electronics, 2026, 48(3): 826-834.
[13]	Zhijun HUANG, Xiao YI, Xin GUAN. Radar and ESM asynchronous association algorithm based on anchoring concept [J]. Systems Engineering and Electronics, 2026, 48(3): 835-845.
[14]	Yingying LI, Hao WU, Feng CHEN, Yuzhuoyue LIN, Xueying WANG, Xin FENG. A geometric positioning technology based on antenna pointing for large azimuth scanning sliding-spot mode of spaceborne SAR [J]. Systems Engineering and Electronics, 2026, 48(3): 846-858.
[15]	Xueying YANG, Gaopeng LI, Yun ZHANG. Semantic information-assisted 3D reconstruction method for satellite-borne multi-view SAR [J]. Systems Engineering and Electronics, 2026, 48(2): 430-446.

Identification method of maritime floating targets based on multiscale permutation entropy features

RichHTML

PDF (PC)

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 15

References 41

Related Articles 15

Recommended Articles

Metrics

Comments

数据序号	测试集样本数	本文方法					波形熵识别方法
数据序号	测试集样本数	TP	FN	FP	TN	准确率/%	TP	FN	FP	TN	准确率/%
1	128	64	1	0	63	99.22	26	39	17	46	56.25
2	128	63	0	0	65	100	46	18	40	24	54.68
3	128	69	1	14	44	88.28	32	32	29	35	52.34
4	128	62	2	2	62	96.88	40	24	34	30	54.68