Radar Emitter Classification Using Self-Organising Neural Network Models

被引：4

作者：

Anjaneyulu, L. ^{[1
]}

Murthy, N. S. ^{[1
]}

Sarma, N. V. S. N. ^{[1
]}

机构：

[1] Natl Inst Technol, Dept ECE, Warangal, Andhra Pradesh, India

来源：

INTERNATIONAL CONFERENCE ON RECENT ADVANCES IN MICROWAVE THEORY AND APPLICATIONS, PROCEEDINGS | 2008年

关键词：

Artificial Neural Networks; EID; Fuzzy ART; ARTMAP; Radar Emitter;

D O I：

10.1109/AMTA.2008.4763033

中图分类号：

Q6 [生物物理学];

学科分类号：

071011 ;

摘要：

This paper presents a Radar Emitter Identification and Classification technique based on Fuzzy ART and ARTMAP Neural Networks. The radar emitter's parameters of RF, PW, PRI, Direction of Arrival(DOA) etc., are taken as inputs for the networks. The network is trained with the available data of the emitter types. After training, the network is used to identify the emitter type by applying the parameters of the emitter as inputs to the neural network. A number of simulations are carried out and the simulated results show that the network quickly and accurately Identify and classify the emitter types.

引用

页码：431 / 433

页数：3

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