基于元学习的雷达小样本目标识别方法及改进

doi:10.12305/j.issn.1001-506X.2022.06.09

Abstract

Abstract:

In recent years, radar target recognition based on deep learning has received great attention. However, there are time constraints and resource constraints in actual combat, and the problem of small sample recognition greatly limits its performance in actual recognition tasks. To solve this problem, based on the meta learning algorithm, this paper improves the performance of new tasks by learning meta knowledge from multiple related tasks, introduces the idea of transfer learning, proposes an improved small sample learning method, and analyzes the application boundary conditions of the method through detailed performance comparison experiments. The experimental results based on the measured high resolution range profile data show that the performance advantage of meta learning method is outstanding only when there are many target categories in the historical accumulated samples and in the case of minimal samples with high correlation with the target task, the proposed method can significantly improve its comprehensive recognition performance.

Key words: radar target recognition, small sample recognition, meta learning, transfer learning, high resolution range profile (HRRP)

CLC Number:

TN957.52

Jingming SUN, Shengkang YU, Jun SUN. Radar small sample target recognition method based on meta learning and its improvement[J]. Systems Engineering and Electronics, 2022, 44(6): 1839-1845.

Figures/Tables 9

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References 21

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参数名称	参数值
目标型号	P1, P2, P3, P4, P5, Q1, Q2, Q3, Q4
目标信噪比/dB	10~50
目标姿态角/(°)	0~180(样本均匀分布)
样本维度	1×1 024
训练集支撑集样本数	6×16 200
训练集查询集样本数	6×1 800
测试集支撑集样本数	3×N
测试集查询集样本数	3×1 800

实验编号	训练集目标型号	测试集目标型号
1	P4, P5, Q1, Q2, Q3, Q4	P1, P2, P3
2	P1, P2, P3, P4, P5, Q4	Q1, Q2, Q3
3	P3, P4, P5, Q2, Q3, Q4	P1, P2, Q1

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Radar small sample target recognition method based on meta learning and its improvement

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