Bind Intra-pulse Modulation Recognition based on Machine Learning in Radar Signal Processing

Abstract

Intra-pulse modulation recognition is one of the radar reconnaissance key technologies; it is especially a hot point of recent researching under low SNR. This thesis propounds a novel way for radar intra-pulse modulation characteristic recognition based on machine learning means of extreme learning machine (ELM), which is widely applied in the region of pattern recognition. As a novel learning framework, the ELM attracts increasing draws in the regions of large-scale computing, high-velocity signal processing, and artificial intelligence. The aim of the ELM is to break the barriers down between the biological learning mechanism and conventional artificial learning techniques and represent a suite of machine learning methods in which hidden neurons need not to be tuned. This algorithm has a trend to provide perfect generalization performance at staggering learning rate. This article focuses on the high frequency (HF) channel environment and Wavelet transform algorithm with the lower computational complexity. The simulation results imply that the ELM could reap a perfectly satisfactory acceptance performance and therefore supplies a substantial ground structure for dealing with intra-pulse modulation challenges in inadequate channel conditions.

Xiaokai Liu is with the School of Information and Communication Engineering, Beijing University of Posts and Telecommunications (BUPT), Beijing, China.