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Moving Target Detection Classifier for Airborne Radar Using SqueezeNet

Moving Target Detection Classifier for Airborne Radar Using SqueezeNet

Authors :
Chengliang Liu
Guifeng Li
Ningning Tong
Yongshun Zhang
Weike Feng
Source :
Journal of Physics: Conference Series. 1883:012003
Publication Year :
2021
Publisher :
IOP Publishing, 2021.

Abstract

Conventional moving target detection methods for airborne radar always need many training range units. To solve this problem, this paper transforms the target detection problem into a multi-classification problem. Firstly, the training dataset is constructed based on a small amount of training range units. Then, a multi-class classifier based on SqueezeNet is constructed. Finally, the trained classifier is used to extract the characteristics of the received space-time data for target detection and parameter estimation. Simulation results show that the SqueezeNet-based airborne radar moving target detection method proposed in this paper can effectively detect the target and estimate its distance, doppler frequency, and other parameters. Compared with the conventional space time adaptive processing method, the proposed method can significantly reduce the number of needed training range units. Compared with the existing target detection method based on classification, the proposed method can effectively improve the accuracy of target detection and parameter estimation.

Details

ISSN :
17426596 and 17426588
Volume :
1883
Database :
OpenAIRE
Journal :
Journal of Physics: Conference Series
Accession number :
edsair.doi...........e1771efcdefc610d2d4046dac4fa2007
Full Text :
https://doi.org/10.1088/1742-6596/1883/1/012003