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Radar signal recovery method based on in-depth study under complex noise environment

A deep learning and radar signal technology, applied in the fields of domain adaptation and deep learning, radar image processing, signal processing, and can solve the problem of no public Doppler radar database.

Active Publication Date: 2018-06-29
TIANJIN UNIV
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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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  • Radar signal recovery method based on in-depth study under complex noise environment
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  • Radar signal recovery method based on in-depth study under complex noise environment

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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 invention relates to a radar signal recovery method based on in-depth study under a complex noise environment, which includes steps of structuring of a radar simulation data set; structuring of anetwork model: recovering a self-adaptive radar signal under the noise environment by a generated confronting network in the in-depth study, and the generated confronting network is composed of a judger and a generator; the judger and the generator are formed by using a thick connection convolution neutral network; training of the generated confronting network: mixing five groups of simulation radar time sequence images under five signal-to-noise ratio environments, generating the training data and training the generated confronting network; recovering the signal of the tested radar image by the generated confronting network.

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...

Claims

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Application Information

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