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Secondary radar signal processing method based on deep four-channel network

A secondary radar and signal processing technology, applied in the field of radar, can solve the problems of data transmission rate, analog signal distortion, digital signal error, etc., to achieve the effect of excellent denoising performance and reduced feature loss

Active Publication Date: 2022-06-07
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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Problems solved by technology

The signal received by ground equipment from space cannot contain only useful signals. During the transmission process, there will be various noises mixed with useful signals. The coexistence of noise and signals in the channel is harmful to transmission. Its existence The analog signal is distorted, the digital signal is errored, and the data transmission rate is also affected

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  • Secondary radar signal processing method based on deep four-channel network
  • Secondary radar signal processing method based on deep four-channel network

Examples

Experimental program
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Embodiment

[0021] This example includes the following steps:

[0022] Step 1: Dataset preparation.

[0023] The total number of samples is 40,000 and the secondary radar response signal with a time step of 512 is used as the sample data. The demodulated response signal with Gaussian white noise with signal-to-noise ratio SNR=5 is used as training data, denoted as X={x ij |i=1,2,...,N; j=1,2,...,K}, where N=40000, K=512. The response signal without noise is used as the training label, denoted as Y={y ij |i=1,2,...,N; j=1,2,...,K}. And it is divided into training set, validation set, and test set according to the ratio of (0.8, 0.1, 0.1).

[0024] Step 2: Preprocess the dataset.

[0025] Randomly scramble the training sample data of the secondary radar response signal, and expand the dimensions of the sample data X and label Y to form a 3D tensor of the form (n, t, f), where n represents the number of samples, and t=512 represents Time step, f represents the number of feature layers....

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Abstract

The invention belongs to the field of radar technology, in particular to a method for denoising secondary radar signals based on a deep four-channel network. The specific scheme is as follows: firstly, prepare the secondary radar response signal data set, and carry out data set division and data set preprocessing. Then a new type of deep four-channel neural network is constructed based on deep learning, which consists of four neural branches connected in parallel. The four channels can fully integrate the learned different deep signal features to reduce feature loss. Finally, the secondary radar test data is input into the deep four-channel neural network to suppress the noise. Timing acknowledgment signals are predicted. The invention has high denoising performance and meets the requirement of noise suppression when the secondary radar receives the response signal.

Description

technical field [0001] The invention belongs to the technical field of radar, in particular to a secondary radar signal processing method based on a deep four-channel network. Background technique [0002] Secondary radar is a radio electronic location and identification system composed of interrogation radar and response radar. The interrogation radar transmits electromagnetic waves, and the response radar is triggered after receiving the interrogation electromagnetic waves, and transmits the response electromagnetic waves. The interrogation radar works according to the received response electromagnetic waves to realize identification and positioning. The interrogation radar of the secondary radar and the response radar on the target work together according to active scanning interrogation and passive response. The interrogation radar is generally fixed on the ground, the pulse of its scanning beam is encoded with the interrogation signal, and the response pulse of the res...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01S13/78G01S13/76G01S7/41G01S7/292
CPCG01S13/78G01S13/76G01S7/292G01S7/418G01S7/417Y02A90/10
Inventor 沈晓峰都雪廖阔潘光松
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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