Research Article
Study of Radar Target Range Profile Recognition Algorithm Based on Optimized Neural Network
@INPROCEEDINGS{10.1007/978-3-030-00557-3_61, author={Xiaokang Guo and Tao Jian and Yunlong Dong and Xiaolong Chen}, title={Study of Radar Target Range Profile Recognition Algorithm Based on Optimized Neural Network}, proceedings={Machine Learning and Intelligent Communications. Third International Conference, MLICOM 2018, Hangzhou, China, July 6-8, 2018, Proceedings}, proceedings_a={MLICOM}, year={2018}, month={10}, keywords={1-D range profile recognition LVQ (Learning Vector Quantization) PSO (Particle Swarm Optimization)}, doi={10.1007/978-3-030-00557-3_61} }- Xiaokang Guo
Tao Jian
Yunlong Dong
Xiaolong Chen
Year: 2018
Study of Radar Target Range Profile Recognition Algorithm Based on Optimized Neural Network
MLICOM
Springer
DOI: 10.1007/978-3-030-00557-3_61
Abstract
Neural network as an important aspect of artificial intelligence has received extensive research and long-term development. Radar target range profile recognition is a commonly used method in radar target recognition, in this paper, it is combined with neural network. The LVQ (Learning Vector Quantization) neural network has excellent classification and identification capabilities. This paper applies it to radar target one-dimensional range image recognition and achieves good results. This paper studies the problem of LVQ neural network sensitive to initial connection weights, and uses PSO (Particle Swarm Optimization) algorithm to optimize it of recognition classification. The experimental results show that the study of radar target range profile recognition algorithm based on optimized neural network can overcome the sensitivity of the LVQ neural network to the initial weight and improve its recognition ability.
