Cross-Modal Recognition Algorithm of Electromagnetic Targets via Siamese Network

ZHANG Wei; WANG Sha-fei; LIN Jing-ran; LI Qiang; SHAO Huai-zong

doi:10.12263/DZXB.20210805

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Cross-Modal Recognition Algorithm of Electromagnetic Targets via Siamese Network

Intelligent game of electromagnetic spectrum | 更新时间：2025-12-08

- Cross-Modal Recognition Algorithm of Electromagnetic Targets via Siamese Network
- ACTA ELECTRONICA SINICA Vol. 50, Issue 6, Pages: 1281-1290(2022)
- 作者机构：
  
  1.电子科技大学信息与通信工程学院，四川成都 611731
  2.电子信息控制重点实验室，四川成都 610036
  3.北方电子设备研究所，北京 100191
  4.鹏城实验室，广东深圳 518055
- 作者简介：
- 基金信息：
- DOI：10.12263/DZXB.20210805
  CLC： TN911;TP181
- Received：29 June 2021，
  
  Revised：2021-12-23，
  
  Published：25 June 2022
- 稿件说明：
移动端阅览
张伟,王沙飞,林静然等.基于孪生网络的电磁目标跨模式识别算法[J].电子学报,2022,50(06):1281-1290.

ZHANG Wei,WANG Sha-fei,LIN Jing-ran,et al.Cross-Modal Recognition Algorithm of Electromagnetic Targets via Siamese Network[J].ACTA ELECTRONICA SINICA,2022,50(06):1281-1290.
张伟,王沙飞,林静然等.基于孪生网络的电磁目标跨模式识别算法[J].电子学报,2022,50(06):1281-1290. DOI： 10.12263/DZXB.20210805.

ZHANG Wei,WANG Sha-fei,LIN Jing-ran,et al.Cross-Modal Recognition Algorithm of Electromagnetic Targets via Siamese Network[J].ACTA ELECTRONICA SINICA,2022,50(06):1281-1290. DOI： 10.12263/DZXB.20210805.

摘要

以深度学习为代表的人工智能技术是解决电磁目标识别问题的一种有效方法.然而，在识别多模式电磁目标时，目标内部不同模式间数据的差异可能掩盖目标个体间的差异，当某种模式训练样本缺失或稀少时，该模式下的目标识别性能会显著下降.为此，提出一种基于孪生网络的电磁目标跨模式识别算法，在度量学习框架下通过优化设计网络结构和损失函数，引导网络在分类学习过程中拉近同一目标各模式数据间的距离，拉远不同目标数据间的距离，并结合邻近判决准则实现多模式电磁目标在非均衡数据集上的跨模式识别.基于实际数据的测试结果表明，在相同数据集和网络规模条件下，所提方法的跨模式识别率较经典卷积神经网络方法和数据增强方法提升20%.

Abstract

The artificial intelligence technology(e.g.

deep learning) is an effective approach to electromagnetic target(ET) recognition. However

in the recognition of multi-mode ETs

when the training samples with a certain mode are missing or rare

the recognition rate with this mode will be significantly degraded. The reason mainly lies in that the data distance between different modes of the same ET may exceed the data distance between different ETs. To remedy this

a cross-modal ET recognition approach via Siamese network is developed in this paper. Following the framework of metric learning

we design the network structure and the loss function carefully

so that the recognition training process intentionally drives the Siamese network to enlarge the data distance between different ETs while shorten the distance between different modes of the same ET. Consequently

the multi-mode ETs can still be successfully recognized by employing certain distance-based decision criterion

even with imbalanced training data sets for different modes. The numerical results based on realistic data show that with same data sets and network size

the cross-modal recognition rate of the proposed approach is 20% higher than that of the classical convolutional neural network approach

and that of the popular data-enhancement approaches.

关键词

Keywords

references

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