Automatic Target Recognition Based On Sequences of High Resolution Radar Range-Profiles

2006 ◽  
Author(s):  
Fang Xiang ◽  
Su Donglin
Keyword(s):  
High Resolution ◽  
Target Recognition ◽  
Automatic Target Recognition ◽  
High Resolution Radar

Automatic target recognition system using high-resolution radar

1996 ◽  
Author(s):  
Steven P. Jacobs ◽  
Joseph A. O'Sullivan ◽  
Mohammad Faisal ◽  
Donald L. Snyder
Keyword(s):  
High Resolution ◽  
Target Recognition ◽  
Recognition System ◽  
Automatic Target Recognition ◽  
High Resolution Radar

scholarly journals A SAR Image Target Recognition Approach via Novel SSF-Net Models

2020 ◽  
Vol 2020 ◽  
pp. 1-9 ◽  
Author(s):  
Wei Wang ◽  
Chengwen Zhang ◽  
Jinge Tian ◽  
Jianping Ou ◽  
Ji Li
Keyword(s):  
High Resolution ◽  
Target Recognition ◽  
Computational Cost ◽  
Automatic Target Recognition ◽  
Sar Image ◽  
Processing Efficiency ◽  
High Resolution Radar ◽  
Public Datasets ◽  
Radar Automatic Target Recognition ◽  
Fully Connected

With the wide application of high-resolution radar, the application of Radar Automatic Target Recognition (RATR) is increasingly focused on how to quickly and accurately distinguish high-resolution radar targets. Therefore, Synthetic Aperture Radar (SAR) image recognition technology has become one of the research hotspots in this field. Based on the characteristics of SAR images, a Sparse Data Feature Extraction module (SDFE) has been designed, and a new convolutional neural network SSF-Net has been further proposed based on the SDFE module. Meanwhile, in order to improve processing efficiency, the network adopts three methods to classify targets: three Fully Connected (FC) layers, one Fully Connected (FC) layer, and Global Average Pooling (GAP). Among them, the latter two methods have less parameters and computational cost, and they have better real-time performance. The methods were tested on public datasets SAR-SOC and SAR-EOC-1. The experimental results show that the SSF-Net has relatively better robustness and achieves the highest recognition accuracy of 99.55% and 99.50% on SAR-SOC and SAR-EOC-1, respectively, which is 1% higher than the comparison methods on SAR-EOC-1.

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