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Article

A Specific Emitter Identification Algorithm under Zero Sample Condition Based on Metric Learning

by 1,2,3, 1,3,4,*, 1,2 and 1,2
1
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
2
Key Laboratory of Network Information System Technology (NIST), Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China
3
School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100190, China
4
National Key Laboratory of Science and Technology on Microwave Imaging, Beijing 100190, China
*
Author to whom correspondence should be addressed.
Academic Editors: Dmitriy Garmatyuk and Chandra Sekhar Pappu
Remote Sens. 2021, 13(23), 4919; https://doi.org/10.3390/rs13234919
Received: 29 October 2021 / Revised: 26 November 2021 / Accepted: 1 December 2021 / Published: 3 December 2021
With the development of information technology in modern military confrontation, specific emitter identification has become a hot and difficult topic in the field of electronic warfare, especially in the field of electronic reconnaissance. Specific emitter identification requires a historical reconnaissance signal as the matching template. In order to avoid being intercepted by enemy electronic reconnaissance equipment, modern radar often has multiple sets of working parameters, such as pulse width and signal bandwidth, which change when performing different tasks and training. At this time, the collected fingerprint features cannot fully match the fingerprint template in the radar database, making the traditional specific emitter identification algorithm ineffective. Therefore, when the working parameters of enemy radar change, that is, when there is no such variable working parameter signal template in our radar database, it is a bottleneck problem in the current electronic reconnaissance field to realize the specific emitter identification. In order to solve this problem, this paper proposes a network model based on metric learning. By learning deep fingerprint features and learning a deep nonlinear metric between different sample signals, the same individual sample signals under different working parameters can be associated. Even if there are no samples under a certain kind of working parameter signal, it can still be associated with the original individual through this network model, so as to achieve the purpose of specific emitter identification. As opposed to the situation in which the traditional specific emitter identification algorithm cannot be associated with the original individual when the signal samples of changing working parameters are not collected, the algorithm proposed in this paper can better solve the problem of changing working parameters and zero samples. View Full-Text
Keywords: electronic warfare; specific emitter identification; metric learning; variable working parameters; zero samples electronic warfare; specific emitter identification; metric learning; variable working parameters; zero samples
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MDPI and ACS Style

Man, P.; Ding, C.; Ren, W.; Xu, G. A Specific Emitter Identification Algorithm under Zero Sample Condition Based on Metric Learning. Remote Sens. 2021, 13, 4919. https://doi.org/10.3390/rs13234919

AMA Style

Man P, Ding C, Ren W, Xu G. A Specific Emitter Identification Algorithm under Zero Sample Condition Based on Metric Learning. Remote Sensing. 2021; 13(23):4919. https://doi.org/10.3390/rs13234919

Chicago/Turabian Style

Man, Peng, Chibiao Ding, Wenjuan Ren, and Guangluan Xu. 2021. "A Specific Emitter Identification Algorithm under Zero Sample Condition Based on Metric Learning" Remote Sensing 13, no. 23: 4919. https://doi.org/10.3390/rs13234919

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