A SAR automatic target recognition method based on multi-view deep learning framework

An automatic target recognition and deep learning technology, applied in the field of synthetic aperture radar automatic target recognition, can solve the problems of deep neural network difficult to be trained effectively and limited recognition performance, and achieve efficient generalization ability, high recognition rate, rapid and accurate recognition Effect

Active Publication Date: 2020-09-15
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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AI Technical Summary

Problems solved by technology

However, this method does not increase the effective identification information of the original SAR image, the improvement of identification performance after sample expansion is very limited, and a large amount of storage space needs to be allocated
The problem that the deep neural network is difficult to be effectively trained in the case of fewer original SAR images has not yet been reasonably solved

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  • A SAR automatic target recognition method based on multi-view deep learning framework
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  • A SAR automatic target recognition method based on multi-view deep learning framework

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Embodiment Construction

[0023] In order to facilitate those skilled in the art to understand the technical content of the present invention, the following terms are explained first.

[0024] Term: Angle Estimation

[0025] Angle of view refers to the attitude information such as pitch angle and azimuth angle in the imaging geometry of synthetic aperture radar. For the specific estimation method, refer to the literature "J.C.Principe, D.Xu, and J.W.Fisher III.Pose estimation in sar using aninformation theoretical criterion.Aerospace / Defense Sensing and Controls. International Society for Optics and Photonics, 1998, pp.218–229.”

[0026] Such as figure 1 Shown is the solution flow chart of the present invention, the technical solution of the present invention is: a SAR automatic target recognition method based on a multi-view deep learning framework, comprising:

[0027] S1. Collect original SAR images; specifically: collect original SAR images with the same resolution and different viewing angles. ...

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Abstract

The invention discloses a SAR automatic target recognition method based on a multi-view deep learning framework, which is applied to the field of radar target recognition. Aiming at the problem that the deep neural network is difficult to be effectively trained when there are few original SAR images, the invention uses the original SAR images Combined with the data acquisition cost and recognition performance requirements in practical applications, a small number of original SAR images are used to generate a large number of multi-view combined samples, which increases the effective recognition information contained in the samples; based on deep learning theory, a multi-input parallel depth The neural network automatically extracts effective features from different perspectives and gives category prediction results to realize rapid and accurate identification of SAR targets. The method of the present invention has the advantages of flexibility, accuracy, high efficiency and strong generalization ability.

Description

technical field [0001] The invention belongs to the field of radar target recognition, in particular to a synthetic aperture radar automatic target recognition technology in the field of radar target recognition. Background technique [0002] Synthetic Aperture Radar (SAR) is a high-resolution microwave imaging radar with all-weather and all-weather working capabilities. Utilization and other fields have extremely high civilian and military value. Due to the electromagnetic scattering characteristics and the coherent imaging mechanism, SAR imaging is sensitive to the target azimuth angle, and there are a large number of coherent spots in the SAR image, which further increases the difference with the optical image and increases the difficulty of manual interpretation. SAR automatic target recognition (Automatic Target Recognition, ATR) is based on the theory of modern signal processing and pattern recognition. It provides strong technical support in many aspects such as pre...

Claims

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Application Information

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Patent Type & AuthorityPatents(China)
IPC IPC(8): G06K9/00G06N3/08G06N3/04
CPCG06N3/084G06V20/13G06N3/045
Inventor杨海光裴季方黄钰林薛媛张寅杨建宇
OwnerUNIV OF ELECTRONICS SCI & TECH OF CHINA