Identification of Radar Emitter Type with Recurrent Neural Networks

被引：3
|
作者：
Apfeld, Sabine [1 ]
Charlish, Alexander [1 ]
Ascheid, Gerd [2 ]
机构：
[1] Fraunhofer FKIE, Dept Sensor Data & Informat Fus, Wachtberg, Germany
[2] Rhein Westfal TH Aachen, Inst Commun Technol & Embedded Syst, Aachen, Germany
来源：
2020 SENSOR SIGNAL PROCESSING FOR DEFENCE CONFERENCE (SSPD) | 2020年
关键词：
D O I：
10.1109/sspd47486.2020.9271988
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
In this paper, we present a method for the identification of different multifunction radar emitter types. It is based on Long Short-Term Memory recurrent neural networks and a previously published hierarchical modelling approach. This approach maps radar pulses to different levels of symbols which can be regarded as parts of a radar language. We evaluate our method with an example emitter that can make use of three different resource management techniques. The results show that it is possible to distinguish between radar types that mainly use the same emission parameters but differ in the resource management method.
引用
收藏
页码：21 / 25
页数：5
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