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A radar signal recovery method in complex noise environment based on deep learning

A technology of deep learning and radar signal, applied in the field of signal processing, domain adaptation and deep learning, radar image processing, can solve the problems such as no public Doppler radar database

Active Publication Date: 2021-09-28
TIANJIN UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The algorithm based on deep learning needs the support of a large amount of diverse training data, and there is currently no public Doppler radar database, so the application of actual radar images is subject to various restrictions

Method used

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  • A radar signal recovery method in complex noise environment based on deep learning
  • A radar signal recovery method in complex noise environment based on deep learning
  • A radar signal recovery method in complex noise environment based on deep learning

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Embodiment Construction

[0025] In order to explain and illustrate the present invention in more detail, the implementation steps are described in detail:

[0026] 1. Construction of radar simulation data set.

[0027] Currently, there is no Doppler radar database with a large amount of rich data. Since deep learning algorithms and machine learning methods require sufficient training data for support, the lack of data brings difficulties to the application of deep learning algorithms on radar data. In order to solve this problem, the present invention utilizes the motion capture database (Motion Capture, MOCAP) of Carnegie Mellon University (CMU) Graphics Lab laboratory as the source of radar simulation data, constructs the simulation radar data set that contains a large amount of radar data. The acquisition of the MOCAP database prevents the human body from infrared sensors, and uses the Vicon motion capture system to capture the motion of the human body during the movement process, so as to obtain t...

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Abstract

The present invention relates to a radar signal recovery method in a complex noise environment based on deep learning, including: the construction of a radar simulation data set; the construction of a network model: using a generation confrontation network in deep learning to realize an adaptive radar signal in a noise environment Restoration, the generative confrontation network is composed of two parts, namely the discriminator and the generator, and the densely connected convolutional neural network is used to form the discriminator and the generator; the training of the generative confrontation network: the five SNR environments Group simulated radar time-frequency images are mixed to generate training data to train the generative adversarial network; use the generative adversarial network to recover the signal of the measured radar image.

Description

technical field [0001] The invention belongs to the field of radar image processing, signal processing, domain self-adaptation and deep learning, and relates to related applications such as signal processing based on Doppler radar data and human body detection. Background technique [0002] Micro-Doppler radar has always been widely used in the military field and plays a vital role. In radar, the moving speed of the detection target is usually far less than the speed of light, so it is considered that the electromagnetic wave travels back and forth twice the distance between the target and the radar, and the radar can realize the distance measurement of the target object by receiving the electromagnetic wave sent before; in addition, the target When the object is moving, the Doppler frequency shift caused by the movement or micro-motion of the target object can be calculated according to the Doppler effect, so as to obtain the speed information of the target object. The abo...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01S7/41
CPCG01S7/41
Inventor 侯春萍黄丹阳杨阳郎玥
Owner TIANJIN UNIV