高海况下应用先验信息的海上小目标检测方法

doi:10.12305/j.issn.1001-506X.2026.01.05

摘要/Abstract

摘要：

在高海况、低信杂比条件下，受海尖峰影响，均值类恒虚警检验统计量常出现长拖尾分布，目标与杂波混叠严重，进而导致检测性能下降。为削弱海尖峰等异常样本影响，本文在考虑大尺度广域海面回波数据时序关联性的前提下，将数据信息的利用从单帧扩展到多帧，按照贝叶斯估计，从多帧数据中挖掘先验信息，并利用序贯估计方法形成贝叶斯检验统计量，与恒定虚警率下的最小值(smallest of constant false alarm rate, SO-CFAR)检测框架相结合，提出一种应用先验分布迭代感知信息的SO-CFAR检测方法。所提检测方法使用幂次变换加强原检验统计量的高斯性，在保证迭代稳定性的条件下，完成历史帧与当前帧的融合。2~5级海况实测数据对比分析结果表明，所提方法在高海况下相比原方法和现有多帧方法，目标检测概率均有明显提升，证实了所提方法在改善检测性能方面的优势。

关键词: 雷达, 目标检测, 先验信息, 贝叶斯估计

Abstract:

Under high sea state and low signal-to-clutter ratio （SCR）, the presence of seaspikes often causes averaging constant false alarm rate （CFAR） test statistics to display long-tail distributions. This leads to significant overlap between targets and clutter, thereby reducing detection performance. To mitigate the impact of such anomalies as sea spikes, this paper takes into account the temporal correlation of large-scale wide-area sea surface echo data and extends the data utilization from single-frame to multi-frame analysis. By adopting a Bayesian estimation, we extract priori information from multiple frames and develop a Bayesian test statistic using a sequential estimation method. The proposed method integrates Bayesian test statistics into the smallest of constant false alarm rate （SO-CFAR） detection framework. The proposed detection method uses power transformation to enhance the Gaussian characteristics of the original test statistic, and accomplishes the fusion of historical and current frames while ensuring iterative stability. The comparative analysis of measured data at sea states 2-5 shows that the proposed method has significantly improved target detection probability compared to original method and multi-frame methods under high sea states, confirming the advantages of the proposed method in improving detection performance.

Key words: radar, target detection, priori information, Bayesian estimation

中图分类号:

TN 957.51

丁昊, 韦继丰, 董云龙, 曹政, 于恒力. 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.

Hao DING, Jifeng WEI, Yunlong DONG, Zheng CAO, Hengli YU. Method for detecting small targets using prior information under high sea states[J]. Systems Engineering and Electronics, 2026, 48(1): 44-55.

图/表 16

表1

图1

图2

图3

图4

图5

图6

图7

表2

图8

图9

图10

图11

图12

表3

图13

参考文献 33

1	PANAGOPOULOS S, SORAGHAN J J. Small-target detection in sea clutter[J]. IEEE Trans. on Geoscience and Remote Sensing, 2004, 42 (7): 1355- 1361. doi: 10.1109/TGRS.2004.827259
2	GRECO M, STINCO P, GINI F, et al. Impact of sea clutter nonstationarity on disturbance covariance matrix estimation and CFAR detector performance[J]. Transactions on Aerospace and Electronic Systems, 2010, 46 (3): 1502- 1513. doi: 10.1109/TAES.2010.5545205
3	SHUI P L, LI D C, XU S W. Tri-feature-based detection of floating small targets in sea clutter[J]. IEEE Trans. on Aerospace and Electronic Systems, 2014, 50 (2): 1416- 1430. doi: 10.1109/TAES.2014.120657
4	SHI Y L, HU Y. mRMR-Tri-ConcaveHull detector for floating small targets in sea clutter[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023, 16, 6799- 6811.
5	吴巍, 薛冰, 刘丹丹. 基于Tri-feature训练的目标与海杂波鉴别算法[J]. 系统工程与电子技术, 2024, 46 (9): 2935- 2940.
	WU W, XUE B, LIU D D. Target and sea clutter identification algorithm based on Tri-feature training[J]. Systems Engineering and Electronics, 2024, 46 (9): 2935- 2940.
6	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
7	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
8	GUAN J, JIANG X Y, LIU N B, et al. Target detection in sea clutter based on feature re-expression using spearman correlation[J]. IEEE Sensors Journal, 2024, 24 (19): 30435- 30450. doi: 10.1109/JSEN.2024.3440065
9	GUO Z X, BAI X H, SHUI P L, et al. Fast dual trifeature-based detection of small targets in sea clutter by using median normalized Doppler amplitude spectra[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023, 16, 4050- 4063. doi: 10.1109/JSTARS.2023.3268181
10	LI D C, SHUI P L. Floating small target detection in sea clutter via normalised Doppler power spectrum[J]. IET Radar, Sonar & Navigation, 2016, 10(4): 699-706.
11	SHI S N, SHUI P L. Sea-surface floating small target detection by one-class classifier in time-frequency feature space[J]. IEEE Trans. on Geoscience and Remote Sensing, 2018, 56 (11): 6395- 6411. doi: 10.1109/TGRS.2018.2838260
12	SHUI P L, GUO Z X, SHI S N. Feature-compression-based detection of sea-surface small targets[J]. IEEE Access, 2019, 8, 8371- 8385.
13	GUO Z X, BAI X H, LI J Y, et al. Fast detection of small targets in high-resolution maritime radars by feature normalization and fusion[J]. IEEE Journal of Oceanic Engineering, 2022, 47 (3): 736- 750. doi: 10.1109/JOE.2021.3133553
14	BAI X H, XU S W, ZHU J N, et al. Floating small target detection in sea clutter based on multifeature angle variance[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023, 16, 9422- 36. doi: 10.1109/JSTARS.2023.3321998
15	XIE J D, XU X J. Phase-feature-based detection of small targets in sea clutter[J]. IEEE Geoscience and Remote Sensing Letters, 2021, 19, 1- 5.
16	XU S W, ZHENG J B, PU J, et al. Sea-surface floating small target detection based on polarization features[J]. IEEE Geoscience and Remote Sensing Letters, 2018, 15 (10): 1505- 1509.
17	XU S W, HE Q. Spatio-temporal dynamic graph attention network-based detector for sea-surface small targets[J]. IEEE Trans. on Aerospace and Electronic Systems , 2024, 60(6): 8010−8021.
18	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
19	SU N Y, CHEN X L, GUAN J, et al. Radar maritime target detection via spatial-temporal feature attention graph convolutional network[J]. IEEE Trans. on Geoscience and Remote Sensing, 2024, 62, 5102615.
20	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): 762-75.
	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.
21	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, 1- 15.
22	YU H L, DING H, CAO Z, et al. A floating small target identification method based on doppler time series information[J]. Remote Sensing, 2024, 16 (3): 505.
23	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, 2021, 60
24	XU S W, MA Y T, BAI X H. Small target detection method in sea clutter based on interframe multi-feature iteration[C]//Proc. of the IEEE 6th International Conference on Signal and Image Processing, 2021: 82–87.
25	SABAHI M F, HASHEMI M M, SHEIKHI A. Radar detection based on Bayesian estimation of target amplitude[J]. IET Radar, Sonar & Navigation, 2008, 2(6): 458–467.
26	LIANG X, YU H, ZOU P J, et al. Multiscan recursive Bayesian parameter estimation of large-scene spatial-temporally varying generalized Pareto distribution model of sea clutter[J]. IEEE Trans. on Geoscience and Remote Sensing, 2022, 60
27	YU H, SHUI P L, ZENG W L , et al. Multiscan recursive bayesian method for parameter estimation of spatially-varying sea clutter models[C]//Proc. of the International Conference on Radar, 2018.
28	XUE J, XU S W, LIU J, et al. Bayesian detection for radar targets in compound-Gaussian sea clutter[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19, 4020805.
29	许述文, 王喆祥, 水鹏朗. 海杂波背景下雷达目标贝叶斯检测算法[J]. 西安电子科技大学学报（自然科学版）, 2021, 48 (2): 15- 26.
	XU S W, WANG Z X, SHUI P L. Bayesian detection algorithm for radar targets in sea clutter background[J]. Journal of Xi’an University of Electronic Science and Technology （Natural Science Edition）, 2021, 48 (2): 15- 26.
30	许述文, 王乐, 曾威良, 等. 逆伽马纹理复合高斯杂波参数的贝叶斯估计方法[J]. 太赫兹科学与电子信息学报, 2019, 17(4): 583−588.
	XU S W, WANG L, ZENG W L, et al. Bayesian estimation method for composite Gaussian clutter parameters with inverse gamma texture [J] Journal of Terahertz Science and Electronic Information, 2019, 17(4): 583−588.
31	邹鲲, 张斌, 王晓薇, 等. 贝叶斯估计器先验模型参数的迭代感知方法[J]. 电子与信息学报, 2015, 37 (6): 1402- 1408.
	ZHOU K, ZHANG B, WANG X W, et al. Iterative perception method for Bayesian estimator’s prior model parameters[J]. Journal of Electronics and Information Technology, 2015, 37 (6): 1402- 1408.
32	KAY S M. Fundamental of statistical signal processing, volume I: estimation theory[M]. New Jersy: Pearson Education Inc. , 1993: 360-365.
33	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.
	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.

序号	数据名称	ASCR/dB	海况
#1_HH	20221115080041-stare-HH	19.97	2
#2_HH	20221114190046-stare-HH	20.32	3
#3_HH	20221113210051-stare-HH	15.42	4
#4_HH	20221112150043-stare-HH	9.50	4
#5_HH	20221113040027-stare-HH	9.73	5
#6_HH	20221112180016-stare-HH	12.33	5

序号	数据名称	ASCR/dB	海况
#1_VV	20221114010041-stare-VV	13.05	3
#2_VV	20221114060105-stare-VV	19.01	3
#3_VV	20221113110015-stare-VV	14.61	4
#4_VV	20221113080027-stare-VV	10.78	5
#5_VV	20221113070023-stare-VV	14.13	5
#6_VV	20221113050014-stare-VV	8.08	5

虚警率	方法	HH极化			VV极化
虚警率	方法	2、3级海况	4级海况	5级海况	3级海况	4级海况	5级海况
10⁻³	传统SO-CFAR	0.8561	0.3238	0.0959	0.5637	0.3852	0.1714
	时序预测方法^[20]	0.8135	0.2355	0.0296	0.4799	0.3394	0.0952
	遗忘因子估计法^[24]	0.9614	0.4466	0.1281	0.7089	0.5801	0.2115
	多帧累积法^[23]	0.9888	0.4973	0.2246	0.7895	0.6668	0.3045
	所提方法	0.9967	0.5905	0.3942	0.8409	0.7341	0.4786
10⁻⁴	传统SO-CFAR	0.7176	0.1811	0.0312	0.3160	0.2030	0.0707
	时序预测方法^[20]	0.6581	0.1162	0.0058	0.2547	0.1850	0.01845
	遗忘因子估计法^[24]	0.9068	0.2947	0.0418	0.5508	0.4133	0.1042
	多帧累积法^[23]	0.9706	0.3373	0.0986	0.6843	0.5383	0.2010
	所提方法	0.9946	0.4090	0.2171	0.7567	0.5853	0.3819

[1]	朱建桦, 周晨, 吕明杰, 王君明, 刘默然, 张新苗, 赵正予. 面向天波超视距雷达应用的电离层杂波统计分析及建模研究[J]. 系统工程与电子技术, 2026, 48(1): 56-66.
[2]	陈海青, 汪刘应, 刘顾, 王龙, 葛超群, 陈孟州. 合成孔径雷达图像去噪算法研究进展[J]. 系统工程与电子技术, 2026, 48(1): 94-105.
[3]	张彬, 许高添, 张廷豪, 李志辉, 何宏强. 分布式合成孔径雷达前视高分辨成像算法[J]. 系统工程与电子技术, 2026, 48(1): 119-131.
[4]	蒋明煜, 张顺生, 肖思瑶. 面向轻量级交叉注意力卷积网络的SAR目标识别[J]. 系统工程与电子技术, 2025, 47(9): 2853-2861.
[5]	张家琦, 刘芳, 曾操, 陶海红, 赵诗华. 联合雷达与光电设备的破裂航迹关联方法[J]. 系统工程与电子技术, 2025, 47(9): 2862-2869.
[6]	李响, 曾顶, 殷君君, 国贤玉, 杨健. 基于梯度融合的极化SAR图像引导滤波[J]. 系统工程与电子技术, 2025, 47(9): 2890-2904.
[7]	朱国辉, 杨琳, 汪洋, 郑陶冶. 一种高速机动平台SAR成像PRF改进设计方法[J]. 系统工程与电子技术, 2025, 47(9): 2905-2912.
[8]	蓝舒尧, 李宇, 张春华, 迟骋, 陈春辉. 垂直阵分集束控影区探测信号发射策略[J]. 系统工程与电子技术, 2025, 47(8): 2454-2462.
[9]	于营, 王春平, 徐金辉, 吕述杭, 付强, 陈明. PE-Net：一种优化剪枝的实时山体滑坡检测网络[J]. 系统工程与电子技术, 2025, 47(8): 2475-2485.
[10]	杨宽, 许小剑. 基于参数外推的超宽带雷达有源极化校准技术[J]. 系统工程与电子技术, 2025, 47(8): 2498-2510.
[11]	罗颖聪, 张磊, 魏少鹏, 孟智超. 联合失配滤波器的近区低旁瓣混沌波形设计算法[J]. 系统工程与电子技术, 2025, 47(8): 2511-2518.
[12]	付卫红, 彭文洪, 刘乃安. 混合注意力优化的SAR图像小目标检测方法[J]. 系统工程与电子技术, 2025, 47(8): 2519-2526.
[13]	倪康, 贾文杰, 邹旻瑞, 郑志忠. 基于动态聚合网络的SAR目标检测[J]. 系统工程与电子技术, 2025, 47(8): 2527-2539.
[14]	薄钧天, 王万田, 李亚星, 张雲硕, 王衡峰, 张嘉毫, 孟进. 波形编码抗主瓣干扰方法带宽限制分析及优化[J]. 系统工程与电子技术, 2025, 47(8): 2540-2548.
[15]	孙一心, 方愚渊, 张磊. 战术机动先验辅助的交互多模型目标跟踪方法[J]. 系统工程与电子技术, 2025, 47(7): 2086-2097.