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

Abstract

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

CLC Number:

TN 957.51

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.

Figures/Tables 16

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References 33

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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

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Method for detecting small targets using prior information under high sea states

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