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Secondary Radar Signal Denoising Method Based on Deep Residual Separation Convolutional Network

A secondary radar, convolutional network technology, applied in the field of radar, can solve the problem of weak signal, affecting the correct decoding and decoding of the response signal, affecting the signal clarity, etc., to achieve the effect of high denoising performance

Active Publication Date: 2022-08-05
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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  • Abstract
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  • Claims
  • Application Information

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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] Step 1: Data preprocessing:

[0029] The secondary radar response signal with Gaussian white noise with signal-to-noise ratio 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 dataset is divided into training set, validation set, and test set with a ratio of (0.6, 0.2, 0.2).

[0031] The training sample data of the response signal is randomly scrambled, and the dimension of the batch data is expanded to form a 3D tensor with a time axis (samples, timesteps, features).

[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 the deep residual separable network model, including three parts: shallow feature extraction, downsampling deep feature extraction, and upsampling feature fusion.

[0035] The shallow feature extraction part is composed of two convo...

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Abstract

The invention belongs to the technical field of radar, in particular to a secondary radar signal denoising processing method based on a deep residual separation convolution network. The invention first preprocesses the secondary radar response signal, and divides, normalizes and vectorizes the data into a data set. Then based on the deep learning method, a deep residual separation convolutional neural network is constructed, which includes three parts: shallow feature extraction, downsampling deep feature extraction, and upsampling feature fusion. A separable convolutional neural network with deep residual connections is used to effectively extract the deep features of secondary radar signals. Finally, the data is input into the network, and the secondary radar response signal is predicted. The invention has high denoising performance in the normal working environment of the secondary radar, can accurately predict the response signal of the secondary radar, and ensures the correct decoding and decoding of the secondary radar signal.

Description

technical field [0001] The invention belongs to the technical field of radar, in particular to a secondary radar signal denoising method of a deep residual separation convolution network. Background technique [0002] Secondary radar has 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 the 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 the actual situation, the signal environment of the secondary radar includes the target, the environmental echo and the noise interference generated by the 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, result...

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Application Information

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Patent Type & Authority Patents(China)
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 ELECTRONICS SCI & TECH OF CHINA