Open AccessMotion classification for radar moving target via STFT and convolution neural network
Author(s) -
Mou Xiaoqian,
Chen Xiaolong,
Su Ningyuan,
Guan Jian
Publication year - 2019
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2019.0179
Subject(s) - computer science , artificial intelligence , radar , convolutional neural network , short time fourier transform , pattern recognition (psychology) , artificial neural network , convolution (computer science) , fourier transform , computer vision , mathematics , fourier analysis , telecommunications , mathematical analysis
Radar moving target detection (MTD) technology is a key technology in the field of radar signal processing. The MTD method of radar has a better performance when applied to uniform motion detection although it has limited performance in other aspects, and it is also difficult to distinguish the type of the moving target. This article presents a new method for detecting and classifying moving targets based on convolutional neural network, which uses the convolutional neural network to learn the motion characteristics of the moving target echo so that realises the detection and classification of moving targets. At first, the authors model the moving target echo. Then the short‐time Fourier transform (STFT) is used to perform time–frequency analysis. The obtained time–frequency graph is used as the input of the convolutional neural network which can automatically learns moving target features through training. Finally, the authors use the trained convolutional neural network model to detect and classify moving targets. The simulation verifies that the method has a great improvement in the detection accuracy rate compared with the traditional MTD.