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

Sichuan Vocational College Of Chemical Technology ,M Liu,Z.H Sun,Air Force Logistics University

doi:10.18287/2412-6179-co-789

Abstract

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.

Highlights

Radar technology can detect and locate long-range targets
This paper mainly studied the target recognition method of high resolution range profile (HRRP), designed an improved neural network model: improved fruit fly optimization algorithmGeneralized Regression Neural Network (IFOA-generalized regression neural network (GRNN)), and compared it with Radial Basis Function (RBF) and GRNN through simulation experiments to understand the target recognition performance of IFOA-GRNN
The results showed that the IFOA-GRNN designed in this study had higher convergence accuracy

Summary

Introduction

Radar technology can detect and locate long-range targets. It is a vital detection tool and has been applied in both civil and military fields [1], especially in military. Kang et al [2] studied Synthetic Aperture Radar (SAR) target recognition, designed an algorithm based on stack automatic encoder, combined unsupervised learning with greedy hierarchical training, and analyzed the target using softmax classifier. They found that the method had a classification accuracy of 95.43 %. This paper mainly studied the target recognition method of high resolution range profile (HRRP), designed an improved neural network model: improved fruit fly optimization algorithmGeneralized Regression Neural Network (IFOA-GRNN), and compared it with Radial Basis Function (RBF) and GRNN through simulation experiments to understand the target recognition performance of IFOA-GRNN. This study makes some contributions to the further development of radar technology

Results

Discussion

Conclusion