access icon openaccess Motion classification for radar moving target via STFT and convolution neural network

This is an open access article published by the IET under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/)
Received 19/02/2019, Accepted 02/05/2019, Published 30/05/2019

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.

Inspec keywords: radar signal processing; time-frequency analysis; learning (artificial intelligence); feature extraction; radar target recognition; convolutional neural nets; radar detection; Fourier transforms; target tracking; radar clutter; radar tracking; radar computing; object detection

Other keywords: short-time Fourier transform; moving target echo; convolution neural network; trained convolutional neural network model; motion classification; radar moving target; traditional MTD method; detection accuracy rate; uniform motion detection; target features; radar signal processing; time-frequency graph

Subjects: Knowledge engineering techniques; Radar equipment, systems and applications; Integral transforms; Signal detection; Integral transforms; Neural computing techniques; Mathematical analysis; Electrical engineering computing

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