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

Wenjuan Ren,Peng Man,Guangluan Xu,Chibiao Ding

doi:10.3390/rs13234919

Abstract

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.

Highlights

With the rapid development of military technology in the world, weapon systems can emerge endlessly
The proposed model takes metric learning as the point of penetration and establishes a deep nonlinear distance metric between different working parameter sample signals of the same individual, so that even for the sample signals with unknown working parameters, it can still be better associated with the original individual than the traditional algorithm
Traditional specific emitter identification algorithms use fixed pre-specified distance metrics, such as Euclidean or Cosine distance metrics, to perform classification, because the fingerprint features that can be used for identification change with the change of working parameters, but the metric used for measurement and evaluation does not change, and the recognition accuracy is greatly reduced

Summary

Introduction

With the rapid development of military technology in the world, weapon systems can emerge endlessly. In the dynamic battlefield environment, how to associate the signal detected from the complex electromagnetic environment with the emitter, the platform, and the weapon system has important military significance. The demand and concept of radar emitter fingerprint identification are generated. Radar emitter fingerprint identification began in the mid1960s, which is generally called specific emitter identification (SEI) [1,2,3] in foreign countries. It refers to receiving electromagnetic signals from unknown radar emitters, analyzing their individual characteristics, and determining the technical level of radar, so as to Remote Sens.

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