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Article

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

by
Zhengjia Wang
1,†,
Yi Han
1,†,
Yiwei Zhang
1,
Junhua Hao
2,3,* and
Yong Zhang
4,5,*
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
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Sensors 2024, 24(2), 583; https://doi.org/10.3390/s24020583
Submission received: 11 December 2023 / Revised: 9 January 2024 / Accepted: 15 January 2024 / Published: 17 January 2024
(This article belongs to the Section Optical Sensors)
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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%.
Keywords: non-cooperation target; micro-Doppler effect; laser coherence detection; deep learning; classification and recognition non-cooperation target; micro-Doppler effect; laser coherence detection; deep learning; classification and recognition

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MDPI and ACS Style

Wang, Z.; Han, Y.; Zhang, Y.; Hao, J.; Zhang, Y. Classification and Recognition Method of Non-Cooperative Objects Based on Deep Learning. Sensors 2024, 24, 583. https://doi.org/10.3390/s24020583

AMA Style

Wang Z, Han Y, Zhang Y, Hao J, Zhang Y. Classification and Recognition Method of Non-Cooperative Objects Based on Deep Learning. Sensors. 2024; 24(2):583. https://doi.org/10.3390/s24020583

Chicago/Turabian Style

Wang, Zhengjia, Yi Han, Yiwei Zhang, Junhua Hao, and Yong Zhang. 2024. "Classification and Recognition Method of Non-Cooperative Objects Based on Deep Learning" Sensors 24, no. 2: 583. https://doi.org/10.3390/s24020583

APA Style

Wang, Z., Han, Y., Zhang, Y., Hao, J., & Zhang, Y. (2024). Classification and Recognition Method of Non-Cooperative Objects Based on Deep Learning. Sensors, 24(2), 583. https://doi.org/10.3390/s24020583

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