Radar Emitter Identification Based on Feedforward Neural Networks
Zhiling Xiao, Zhenya Yan · 2020 IEEE 4th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC) · 2020
The aim of the article is implement radar emitter Identification. This paper applies neural network classifier in identification, and proposes a approach based on combination of multiple classifiers to improve the training efficiency. In the study, feedforward neural networks are utilized for identifying radar radiation sources, and the parameters of radar signals (direction of arrival, pulse width, pulse repetition frequency and radar frequency) are selected as features for training classifier. In order to reduce the time for training classifier, the outputs are divided into different groups and different classifiers are generated for each group. The experimental results reveal that feedforward neural networks show outstanding performance in radar emitters recognition, and the scheme presented can effectively save time for training.