A semi-supervised transfer learning recognition method for radar compound jamming under small samples

Jinqiang WANG; Minhong SUN; Xianghong TANG; Zhaoyang QIU; Deguo ZENG

doi:10.11959/j.issn.1000-0801.2023182

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A semi-supervised transfer learning recognition method for radar compound jamming under small samples

Research and Development | 更新时间：2024-06-05

- A semi-supervised transfer learning recognition method for radar compound jamming under small samples
- Telecommunications Science Vol. 39, Issue 10, Pages: 15-28(2023)
- 作者机构：
  
  1. 杭州电子科技大学通信工程学院，浙江杭州 310018
  2. 中国航天科工集团八五一一研究所，江苏南京 210007
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.1000-0801.2023182
  CLC： TN975
- Published Online：2023-10，
  
  Published：20 October 2023
- 稿件说明：
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Jinqiang WANG, Minhong SUN, Xianghong TANG, et al. A semi-supervised transfer learning recognition method for radar compound jamming under small samples[J]. Telecommunications science, 2023, 39(10): 15-28.
DOI：

Jinqiang WANG, Minhong SUN, Xianghong TANG, et al. A semi-supervised transfer learning recognition method for radar compound jamming under small samples[J]. Telecommunications science, 2023, 39(10): 15-28. DOI： 10.11959/j.issn.1000-0801.2023182.

摘要

针对雷达复合干扰信号种类越来越多以及训练样本过少难以令深度学习模型达到最优状态的问题，提出一种在小样本下雷达复合干扰半监督迁移学习识别的方法，通过未带标签样本来解决标签样本难以获取而导致网络训练精度不高的问题，将在单一干扰数据集预训练后得到的特征提取器和分类器迁移到小规模复合干扰数据集上，并利用权重印记和半监督学习对模型进行微调，通过所提出的最近邻相关性损失（nearest neighbor correlation loss，NNCL）来优化模型参数。实验结果表明，在干噪比为10 dB、新类复合干扰信号带标签样本仅有5个时，模型可达93.20%的识别准确率。

Abstract

Aiming at the problem that more and more kinds of radar compound jamming signals and too few training samples were difficult to make the deep learning model reach the optimal state

a semi-supervised transfer learning recognition method for radar compound jamming under small samples was proposed

which solved the problem of low network training accuracy caused by the difficulty in obtaining labeled samples through unlabeled samples.The feature extractor and classifier obtained after pre-training of single jamming data set were transferred to small-scale compound jamming data set

and the model was fine-tuning by using weight imprinting and semi-supervised learning.The model parameters were optimized by the proposed nearest neighbor correlation loss nearest neighbor correlation loss (NNCL).The experimental results show that the recognition accuracy of the model can reach 93.20% when the jamming-to-noise ratio is 10 dB and there are only 5 labeled samples of the new class of compound jamming signals.

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