• 电磁频谱智能+ •

基于孪生网络的电磁目标跨模式识别算法

1. 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
• 收稿日期:2021-06-29 修回日期:2021-12-23 出版日期:2022-06-25
• 通讯作者:
• 林静然
• 作者简介:
• 张 伟 男，1985年出生，山东泰安人.高级工程师，博士研究生.主要研究方向为非合作通信信号处理、电子对抗.
王沙飞 男，1964年出生，河北张家口人.研究员，博士生导师.主要研究方向为非合作通信信号处理、电子对抗.
林静然（通讯作者） 男，1978年出生，重庆人.教授，博士生导师.主要研究方向为电磁频谱智能管控与优化.
利 强 男，1982年出生，四川成都人.副教授，博士生导师.主要研究方向为无线通信信号处理与优化.
邵怀宗 男，1969年出生，四川巴中人.教授，博士生导师.主要研究方向为通信和雷达信号处理.
• 基金资助:
• 国家自然科学基金 (U19B2028)

Cross-Modal Recognition Algorithm of Electromagnetic Targets via Siamese Network

ZHANG Wei1,2, WANG Sha-fei3, LIN Jing-ran1,4(), LI Qiang1,4, SHAO Huai-zong1,4

1. 1.School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China
2.Science and Technology on Electronic Information Control Laboratory, Chengdu, Sichuan 610036, China
3.Northern Institute of Electronic Equipment of China, Beijing 100191, China
4.Peng Cheng Laboratory, Shenzhen, Guangdong 518055, China
• Received:2021-06-29 Revised:2021-12-23 Online:2022-06-25 Published:2022-06-25
• Corresponding author:
• LIN Jing-ran
• Supported by:
• National Natural Science Foundation of China (U19B2028)

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.

Extended Abstract
The artificial intelligence technology (e.g., deep learning) is an effective approach to electromagnetic target (ET) recognition. However, the design of current ETs is increasingly complex, and most of them can flexibly work on a variety of modes with huge differences, which brings new challenges to ET recognition. Specifically, 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, or the same target working in different modes may even be mistakenly recognized as multiple targets. 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. As a result, the common features among the signals with different modes of the same target can be extracted, and hence 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. On this basis, a joint-decision-based feedback mechanism is further designed. When the test data with the rare mode arrives, multiple samples are used for joint decision. Then, the new-coming data is marked according to the joint decision results, and the network is re-trained with the new-labeled data. Consequently, the network gradually improves the recognition performance with the rare mode. 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.