基于时频图自主学习的海面小目标检测

doi:10.3969/j.issn.1001-506X.2021.01.05

Abstract

Abstract:

Multi-dimensional feature detection method has been successfully applied to sea-surface small target detection. Due to the limitation of artificial feature extraction, detection problem is converted into two classification problems and the target detection method based on deep learning of time-frequency (TF) graph is proposed. Fristly, one-dimensional observation echo is transformed into two-dimensional TF domain, and the whitening pre-treating is carried out by normalized TF graph. Second, a semi-simulated database with target echo is established to solve the problem of unbalanced two training samples. Then, transfer learning model is built to independently study the properties of TF graph, which has the advantages of deep network structure and reducing training cost. Finally, the probabilities of two class serve as statistics to obtain the decision region with controllable false alarm rate. Experimental results by IPIX datasets show that the proposed detector can deeply mine the differences between target and clutter, which can effectively improve the detection capability of sea-surface small targets at low signal-to-clutter ratio case.

Key words: sea clutter, target detection, time-frequency (TF) graph, transfer learning

CLC Number:

TN911.23

Sainan SHI, Zeyuan DONG, Jing YANG, Chunjiao YANG. Sea-surface small target detection based on autonomic learning of time-frequency graph[J]. Systems Engineering and Electronics, 2021, 43(1): 33-41.

Figures/Tables 12

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References 29

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数据名称	#26	#30	#310	#311
编号	1	2	3	4
目标单元	7	7	7	7
影响单元	6~8	6~8	6~9	6~9
风速/(km/h)	9	19	33	33
有效浪高/m	1.1	0.9	0.9	0.9
ASCR/dB	4.3	-0.3	2.3	11.9
多普勒距离	0.66	0.68	0.88	0.95

检测器名称	比较因子
检测器名称	#26	#30	#310	#311	训练时间/s	测试时间/s
AlexNet^[20]	0.165	0.094	0.506	0.877	-	-
AlexNet+NTFD	0.168	0.118	0.764	0.892	1 084	0.67
ALTG检测器	0.426	0.140	0.885	0.942	1 681	0.09

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Sea-surface small target detection based on autonomic learning of time-frequency graph

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