High Resolution Radar Target Recognition Based on Distributed Glint

2013 ◽  
pp. 977-984
Author(s):  
Baoguo Li ◽  
Zongfeng Qi ◽  
Ying Zhou ◽  
Jing Lei
Keyword(s):  
High Resolution ◽  
Target Recognition ◽  
Radar Target ◽  
Radar Target Recognition ◽  
High Resolution Radar

scholarly journals High-Resolution Radar Target Recognition via Inception-Based VGG (IVGG) Networks

2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Wei Wang ◽  
Chengwen Zhang ◽  
Jinge Tian ◽  
Xin Wang ◽  
Jianping Ou ◽  
...  
Keyword(s):  
Neural Networks ◽  
High Resolution ◽  
Convolutional Neural Networks ◽  
Target Recognition ◽  
Radar Target ◽  
Range Resolution ◽  
Radar Target Recognition ◽  
High Resolution Radar ◽  
High Range ◽  
Visual Geometry

Aiming at high-resolution radar target recognition, new convolutional neural networks, namely, Inception-based VGG (IVGG) networks, are proposed to classify and recognize different targets in high range resolution profile (HRRP) and synthetic aperture radar (SAR) signals. The IVGG networks have been improved in two aspects. One is to adjust the connection mode of the full connection layer. The other is to introduce the Inception module into the visual geometry group (VGG) network to make the network structure more suik / for radar target recognition. After the Inception module, we also add a point convolutional layer to strengthen the nonlinearity of the network. Compared with the VGG network, IVGG networks are simpler and have fewer parameters. The experiments are compared with GoogLeNet, ResNet18, DenseNet121, and VGG on 4 datasets. The experimental results show that the IVGG networks have better accuracies than the existing convolutional neural networks.

RADAR TARGET RECOGNITION BASED ON PARAMETERIZED HIGH RESOLUTION RANGE PROFILES

2000 ◽  
Vol 14 (07) ◽  
pp. 979-986 ◽  
Author(s):  
XUEJUN LIAO ◽  
ZHENG BAO
Keyword(s):  
High Resolution ◽  
Data Reduction ◽  
Target Recognition ◽  
Recognition System ◽  
Experimental Results ◽  
Radar Target ◽  
Discriminative Power ◽  
Power Performance ◽  
Radar Target Recognition ◽  
Significant Data

A new scheme of radar target recognition based on parameterized high resolution range profiles (PHRRP) is presented in this paper. A novel criterion called generalized-weighted-normalized correlation (GWNC) is proposed for measuring the similarity between PHRRP's. By properly choosing the parameter of the mainlobe width in GWNC, aspect sensitivity of PHRRP's can be reduced without sacrificing their discriminative power. Performance of the scheme is evaluated using a dataset of three scaled aircraft models. The experimental results show that by using GWNC, only a small number of most dominant scatterers can achieve the same recognition rates as HRRP's, thus leading to a significant data reduction for the recognition system.

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