Secondary radar signal processing method based on two-channel residual error deep neural network

Abstract

The invention belongs to the technical field of radars, and particularly relates to a secondary radar signal processing method based on a two-channel residual error deep neural network. The method comprises the following steps: firstly, acquiring secondary radar response signal sample data and preprocessing a data set; then constructing the novel two-channel residual error deep neural network based on a deep learning method, the two-channel residual error deep neural network is composed of two feature extraction channels, each channel is subjected to residual error addition for multiple times,and residual error connection is carried out between the two channels; inputting the training set and the verification set, training the residual two-channel deep network, and stopping training whenthe parameters are optimal; and finally, inputting the test data into the network, and predicting a secondary radar response signal. The network model can reduce information loss and fully extract deep features of secondary radar signals. The method is excellent in denoising performance, can accurately predict the secondary radar time sequence signal, and meets the noise inhibition demands.

Description

technical field [0001] The invention belongs to the technical field of radar, and specifically relates to a secondary radar signal processing method based on a two-channel residual deep neural network. Background technique [0002] The secondary radar system is the main surveillance method for national defense and domestic civil aviation. During the transmission process, it will be interfered by noise, which will affect the clarity of signal transmission, and also lead to a decrease in the stability and reliability of radio wave transmission. Although traditional denoising methods are effective, there are still noise residues, which affect signal detection. Nowadays, machine learning technology is developing rapidly. As a branch of machine learning, deep learning and convolutional neural network have made great achievements in the fields of AlphaGo man-machine game and network big data analysis. How to use the powerful functions of deep learning and CNN to remove signal noi...

AI Technical Summary

Problems solved by technology

[0002] The secondary radar system is the main surveillance method for national defense and domestic civil aviation. During the transmission process, it will be interfered by noise, which will affect the clarity of signal transmission, and also lead to a decrease in the stability and reliability of radio wave transmission.

Although traditional denoising methods are effective, there are still noise residues, which affect signal detection

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