The Radar Target Recognition Research Based on Improved Neural Network Algorithm

2014 ◽  
Author(s):  
Duan Peipei ◽  
Li Hui
Keyword(s):  
Neural Network ◽  
Target Recognition ◽  
Radar Target ◽  
Radar Target Recognition ◽  
Network Algorithm ◽  
Neural Network Algorithm

Radar target recognition based on neural network

1996 ◽  
Vol 13 (1) ◽  
pp. 1-10
Author(s):  
Zhao Qun ◽  
Bao Zheng ◽  
Ye Wei
Keyword(s):  
Neural Network ◽  
Target Recognition ◽  
Radar Target ◽  
Radar Target Recognition

scholarly journals Application of the fruit fly optimization algorithm to an optimized neural network model in radar target recognition

2021 ◽  
Vol 45 (2) ◽  
pp. 296-300
Author(s):  
M. Liu ◽  
Z.H. Sun
Keyword(s):  
Neural Network ◽  
Optimization Algorithm ◽  
Target Recognition ◽  
Recognition Rate ◽  
Fruit Fly ◽  
Generalized Regression Neural Network ◽  
Radar Target ◽  
Fruit Fly Optimization Algorithm ◽  
Fruit Fly Optimization ◽  
Radar Target Recognition

With the development of computer technology, there are more and more algorithms and models for data processing and analysis, which brings a new direction to radar target recognition. This study mainly analyzed the recognition of high resolution range profile (HRRP) in radar target recognition and applied the generalized regression neural network (GRNN) model for HRRP recognition. In order to improve the performance of HRRP, the fruit fly optimization algorithm (FOA) algorithm was improved to optimize the parameters of the GRNN model. Simulation experiments were carried out on three types of aircraft. The improved FOA-GRNN (IFOA-GRNN) model was compared with the radial basis function (RBF) and GRNN models. The results showed that the IFOA-GRNN model had a better convergence accuracy, the highest average recognition rate (96.4 %), the shortest average calculation time (275 s), and a good recognition rate under noise interference. The experimental results show that the IFOA-GRNN model has a good performance in radar target recognition and can be further promoted and applied in practice.

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