Micro-motion Forms Classification of Space Cone-shaped Target Based on Convolution Neural Network
Year: 2020
Type: article
Abstract: In this paper, the echo models with different micro-motion forms (spin, tumbling, precession, and nutation) of space cone-shaped target are built. Different from the ideal point scatterers model, the radar echo contains the contribution from the complex radar cross section (RCS) of point scatterer vs aspect angle. And a convolution neural network (CNN) model for micromotion forms classification based on the micro-Doppler characteristics in spectrograms is presented. The simulation results show that our method can discriminate different micro-motion forms effectively and the overall accuracy is 97.24%. Different levels of additive white Gaussian noise are added to simulate noise-contaminated radar echo. It has been found that the presented method has a stronger anti-noise ability than support vector machine (SVM). When the Signal-to-Noise Ratio (SNR) of Gaussian white noise is 10 dB, the overall accuracy of our algorithm is 29.79% higher than that of SVM.
Authors: Gaogui Xu, Hongcheng Yin, Chunzhu Dong
Institution: Communication University of China
Language: English
Subfield: Aerospace Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W3200102517