Automatic modulation classification method based on improved stacked hourglass neural network

Abstract

The invention discloses an automatic modulation classification method based on an improved stacked hourglass neural network. The method comprises the following steps: obtaining a modulation signal as original data, and carrying out normalization processing on the original data; the method comprises the following steps of: acquiring feature information of a modulation signal by adopting two convolution kernels with different shapes, and connecting the two acquired convolution features in a channel dimension to form multi-local feature information; receiving multi-local feature information and adopting an initial convolution module to increase the number of feature channels; the multi-local feature information with the increased number of feature channels is subjected to end-to-end separation in sequence by adopting four-stage hourglass module stacking; wherein each hourglass module takes the bottleneck layer as a basic unit, the channel dimension change is carried out in the bottleneck layer, and each hourglass module filters the channel by adopting a channel attention mechanism in the down-sampling stage and the up-sampling stage. The method is improved on the basis of the baseline network of the stacked hourglass neural network, and the modulation recognition accuracy can be remarkably improved.

Description

technical field [0001] The invention relates to a wireless communication signal processing technology, in particular to an automatic modulation classification method based on an improved stacked hourglass neural network. Background technique [0002] In the field of wireless communication, automatic modulation recognition (Automatic Modulation Recognition, AMR) of communication signals is a key technology in the field of signal processing and pattern recognition, and it is also a difficult technology. This technology is widely used in military and civilian fields, and has important application value and scientific significance. For example, in the military field, scouts need communication signal modulation pattern recognition technology to modulate and identify the enemy's wireless communication signals, and then implement targeted interference and monitoring, so as to achieve effective electronic countermeasures; in the civilian field, the government and related departments...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to solve the problem of low accuracy of automatic modulation recognition for communication signals in the field of wireless communication, and to provide an automatic modulation classification method based on the improved stacked hourglass neural network, which is based on the baseline network of the stacked hourglass neural network. Improvements on the basis can significantly improve the accuracy of modulation recognition

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