Classification and Recognition Method of Non-Cooperative Objects Based on Deep Learning-Reference-Cited by-同舟云学术

Classification and Recognition Method of Non-Cooperative Objects Based on Deep Learning

Published:2024-01-17 Issue:2 Volume:24 Page:583
ISSN:1424-8220
Container-title:Sensors
language:en
Short-container-title:Sensors

Author:

Wang Zhengjia¹,Han Yi¹,Zhang Yiwei¹,Hao Junhua²³^ORCID,Zhang Yong⁴⁵

Affiliation:

1. Institute of Precision Acousto-Optic Instrument, School of Instrumentation Science and Engineering, Harbin Institute of Technology, Harbin 150080, China

2. School of Precision Instruments and Opto-Electronics Engineering, Key Lab of Optoelectronic Information Technology (Ministry of Education), and Key Lab of Micro-Opto-Electro-Mechanical Systems (MOEMS) Technology (Ministry of Education), Tianjin University, Tianjin 300072, China

3. Department of Physics, Tianjin Renai College, Tianjin 301636, China

4. National Key Laboratory of Science and Technology on Tunable Laser, Harbin Institute of Technology, Harbin 150080, China

5. Department of Optoelectronic Information Science and Technology, School of Astronautics, Harbin Institute of Technology, Harbin 150080, China

Abstract

Accurately classifying and identifying non-cooperative targets is paramount for modern space missions. This paper proposes an efficient method for classifying and recognizing non-cooperative targets using deep learning, based on the principles of the micro-Doppler effect and laser coherence detection. The theoretical simulations and experimental verification demonstrate that the accuracy of target classification for different targets can reach 100% after just one round of training. Furthermore, after 10 rounds of training, the accuracy of target recognition for different attitude angles can stabilize at 100%.

Funder

National Natural Science Foundation of China

Tianjin Research Innovation Project for Postgraduate Students

Publisher

MDPI AG

Link

https://www.mdpi.com/1424-8220/24/2/583/pdf

Reference27 articles.

1. Space-Based Detection of Space Debris by Photometric and Polarimetric Characteristics;Pang;Chin.-Soc.-Opt.-Eng.,2017

2. Review and comparison of active space debris capturing and removal methods;Shan;Prog. Aerosp. Sci.,2016

3. A monocular structured light vision method for pose determination of large non-cooperative satellites;Gao;Int. J. Control. Autom. Syst.,2016

4. Vision-based position and pose determination of non-cooperative target for on-orbit servicing;Hu;Multimed. Tools Appl.,2020

5. 2D-3D Pose Estimation of Heterogeneous Objects Using a Region Based Approach;Hexner;Int. J. Comput. Vis.,2016

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