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

A technology of deep neural network and secondary radar, which is applied in the field of secondary radar signal processing based on two-channel residual deep neural network, can solve the problem of affecting signal transmission clarity, reduction of radio wave transmission stability and reliability, noise residue, etc. problem, to achieve excellent denoising performance, reduce information loss, and meet the needs of noise suppression

Inactive Publication Date: 2020-09-11
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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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 signa

Method used

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

Examples

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Embodiment

[0020] This example includes the following steps:

[0021] The first step is to obtain a sample data set: the secondary radar response timing signal with a total number of samples of 60,000 and a time step of 512 is used as sample data. The secondary radar response timing signal after adding Gaussian white noise SNR=5 and demodulation is used as the training data set, denoted as Among them, N=60000 represents the number of signal samples, and Z=512 represents the signal time step. The original secondary radar response signal without adding noise is used as the training label, denoted as And the sample data is divided into training set, verification set and test set according to the ratio of (0.6,0.2,0.2).

[0022] The second step is to preprocess the data set: randomly scramble the secondary radar training sample data, expand the dimension of the sample data and labels, and form a 3D tensor of the form (n, t, g), where n represents The number of samples, t represents the ...

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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...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06N3/04G06N3/08G01S7/02G01S13/74
CPCG06N3/08G01S7/023G01S13/74G06N3/045G06F2218/04
Inventor 沈晓峰都雪廖阔王子健
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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