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

系统工程与电子技术 ›› 2026, Vol. 48 ›› Issue (6): 1880-1892.doi: 10.12305/j.issn.1001-506X.2026.06.10

基于多尺度排列熵特征的海上漂浮目标识别方法

丁昊¹(), 唐天宇², 曹政¹^,*(), 于恒力¹, 董云龙¹

1. 海军航空大学，山东烟台 264001
2. 中国人民解放军 91566部队，辽宁大连 116041

收稿日期:2025-02-26 修回日期:2025-05-06 出版日期:2026-06-25 发布日期:2025-07-30
通讯作者: 曹政 E-mail:331299929@qq.com;caoziyuan0707@163.com
作者简介:丁　昊（1988—），男，副教授，博士，主要研究方向为海杂波特性认知与抑制、海杂波中目标检测识别
唐天宇（2001—），男，助理工程师，学士，主要研究方向为雷达目标识别
于恒力（1993—），男，副教授，博士，主要研究方向为海上微弱目标检测
董云龙（1974—），男，教授，博士，主要研究方向为多传感器信息融合
基金资助:
国家自然科学基金（62388102，62101583）资助课题

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

Hao DING¹(), Tianyu TANG², Zheng CAO¹^,*(), Hengli YU¹, Yunlong DONG¹

1. Naval Aviation University，Yantai 264001，China
2. Unit 91566 of the PLA，Dalian 116041，China

Received:2025-02-26 Revised:2025-05-06 Online:2026-06-25 Published:2025-07-30
Contact: Zheng CAO E-mail:331299929@qq.com;caoziyuan0707@163.com

摘要/Abstract

摘要：

现有雷达海上漂浮目标识别方法通常需要应用双极化、长时观测数据等信息，导致应用场景受限。针对该问题，以海上目标耐波性理论为支撑，通过分析发现漂浮目标在海浪扰动下呈现的排序起伏变化和随机程度具有特定规律，利用多尺度排列熵方法进行特征提取，可清晰呈现漂浮目标时序扰动频率高、排列模式多样的规律特点。基于该机理提出一种应用多尺度排列熵特征的海上漂浮目标识别方法。以衡量两类目标样本可分性的巴氏距离为定量指标，对特征提取参数中的嵌入维数和粗粒化尺度进行优化处理，实现特征可分性改善。2级和4级海况实测数据验证结果表明，在0.5 s观测时间上，所提方法对漂浮目标识别的平均准确率达96.1%，优于已有方法，且在较短观测时间、不同观测视角、无需双极化等条件下均可实现较高的识别准确率，具有稳健性。

关键词: 雷达, 漂浮目标, 多尺度排列熵, 目标识别

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

中图分类号:

TN 957.51

丁昊, 唐天宇, 曹政, 于恒力, 董云龙. 基于多尺度排列熵特征的海上漂浮目标识别方法[J]. 系统工程与电子技术, 2026, 48(6): 1880-1892.

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.

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参考文献 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

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基于多尺度排列熵特征的海上漂浮目标识别方法

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

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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