Secondary radar signal denoising method based on deep residual separation convolutional network

A secondary radar and convolutional network technology, applied in the radar field, can solve problems such as weak signals, affecting the correct decoding and decoding of response signals, and affecting signal clarity, and achieve high denoising performance

Active Publication Date: 2020-09-01
UNIV OF ELECTRONIC SCI & TECH OF CHINA
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Problems solved by technology

The noise is superimposed on the useful signal. When the transmission power is constant and the transmission loss is large, the signal becomes quite weak, which seriously affects the clarity of signal reception, reduces the stability and reliability of radio wave transmission, and greatly affects the response signal. correct decoding of

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  • Secondary radar signal denoising method based on deep residual separation convolutional network
  • Secondary radar signal denoising method based on deep residual separation convolutional network
  • Secondary radar signal denoising method based on deep residual separation convolutional network

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Embodiment

[0028] The first step: data preprocessing:

[0029] The secondary radar response signal with Gaussian white noise with SNR=5 is used as the training data set, and the response signal without noise is used as the training label. The total number of data is 60000.

[0030] The data set is divided into training set, validation set, and test set at the ratio of (0.6, 0.2, 0.2).

[0031] Randomly scramble the response signal training sample data, and expand the dimensionality of the batch data to form a 3D tensor (samples, timesteps, features) with a time axis.

[0032] Normalize the training data.

[0033] Step 2: Build a deep residual separable neural network:

[0034] figure 1 It is a schematic diagram of the structure of a deep residual separable network model, including three parts: shallow feature extraction, down-sampling deep feature extraction, and up-sampling feature fusion.

[0035] The shallow feature extraction part is composed of two convolutional neural networks CNN, which can ...

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Abstract

The invention belongs to the technical field of radars, and particularly relates to a secondary radar signal denoising processing method based on a deep residual separation convolutional network. Themethod comprises the steps of firstly, preprocessing a secondary radar response signal, and carrying out data set division, normalization and vectorization on data; and then, based on a deep learningmethod, constructing a deep residual separation convolutional neural network which comprises three parts of shallow feature extraction, down-sampling deep feature extraction and up-sampling feature fusion; adopting separable convolutional neural network connected by the deep residual error to effectively extract the deep features of the secondary radar signal; and finally, inputting the data intothe network to predict a secondary radar response signal. The method has very high denoising performance in a normal working environment of the secondary radar, can accurately predict the secondary radar response signal, and ensures correct decoding of the secondary radar signal.

Description

Technical field [0001] The invention belongs to the field of radar technology, and specifically is a method for denoising secondary radar signals of a deep residual separation convolution network. Background technique [0002] Secondary radars have been widely used in many aspects such as air traffic control, identification of friend or foe and beacon tracking. Compared with the primary radar, the secondary radar using monopulse technology can more accurately measure the distance and azimuth of the air target, and find the target by responding to the interrogation signal. In actual situations, the signal environment of the secondary radar includes the target, environmental echo and noise interference caused by man-made active and passive interference. Noise is superimposed on the useful signal. When the transmission power is constant and the transmission loss is large, the signal becomes quite weak, which seriously affects the clarity of signal reception, reduces the stability a...

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Application Information

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IPC IPC(8): G01S13/78G01S13/76G01S13/91G01S7/41G01S7/292
CPCG01S13/78G01S13/76G01S13/91G01S7/418G01S7/417G01S7/292
Inventor 沈晓峰都雪廖阔许天奇
Owner UNIV OF ELECTRONIC SCI & TECH OF CHINA
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