Ship recognition based on HRRP via multi-scale sparse preserving method

doi:10.23919/JSEE.2023.000136

Journal of Systems Engineering and Electronics ›› 2024, Vol. 35 ›› Issue (3): 599-608.doi: 10.23919/JSEE.2023.000136

• DEFENCE ELECTRONICS TECHNOLOGY • Previous Articles

Ship recognition based on HRRP via multi-scale sparse preserving method

Xueling YANG¹^,²^,³(), Gong ZHANG¹^,²^,*(), Hu SONG³()

¹ College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
² Key Lab of Radar Imaging and Microwave Photonics, Ministry of Education, Nanjing 210016, China
³ Nanjing Marine Radar Institute, Nanjing 210016, China

Received:2022-06-13 Accepted:2023-10-09 Online:2024-06-18 Published:2024-06-19
Contact: Gong ZHANG E-mail:yangxueling@nuaa.edu.cn;gzhang@nuaa.edu.cn;andysonghu@126.com
About author:
YANG Xueling was born in 1986. He received his B.S. and M.S. degrees from the College of Mathematical Sciences, Harbin Engineering University, Heilongjiang, China, in 2008 and 2011, respectively. He is a Ph.D. candidate in Nanjing University of Aeronautics and Astronautics. He is a senior engineer with the Nanjing Marine Radar Institute. His current research interests are radar target recognition and radar signal processing. E-mail: yangxueling@nuaa.edu.cn

ZHANG Gong was born in 1964. He received his Ph.D. degree from Nanjing University of Aeronautics and Astronautics (NUAA) in 2002. He is a professor in the College of Electronics and Information Engineering, NUAA. He is a member of the Committee of Electromagnetic Information, Chinese Society of Astronautics (CEI-CSA), and a senior member of the Chinese Institute of Electronics (CIE). His research interests are synthetic aperture rader (SAR) image processing, target detection, and target recognition. E-mail: gzhang@nuaa.edu.cn

SONG Hu was born in 1980. He received his M.S. and Ph.D. degrees from the Communication System, Nanjing University of Science and Technology, Nanjing, China, in 2005 and 2020, respectively. He is a senior engineer with the Nanjing Marine Radar Institute. His current research interest is radar target recognition. E-mail: andysonghu@126.com
Supported by:
This work was supported by the National Natural Science Foundation of China (62271255;61871218), the Fundamental Research Funds for the Central University (3082019NC2019002), the Aeronautical Science Foundation (ASFC-201920007002), and the Program of Remote Sensing Intelligent Monitoring and Emergency Services for Regional Security Elements.

Abstract

Abstract:

In order to extract the richer feature information of ship targets from sea clutter, and address the high dimensional data problem, a method termed as multi-scale fusion kernel sparse preserving projection (MSFKSPP) based on the maximum margin criterion (MMC) is proposed for recognizing the class of ship targets utilizing the high-resolution range profile (HRRP). Multi-scale fusion is introduced to capture the local and detailed information in small-scale features, and the global and contour information in large-scale features, offering help to extract the edge information from sea clutter and further improving the target recognition accuracy. The proposed method can maximally preserve the multi-scale fusion sparse of data and maximize the class separability in the reduced dimensionality by reproducing kernel Hilbert space. Experimental results on the measured radar data show that the proposed method can effectively extract the features of ship target from sea clutter, further reduce the feature dimensionality, and improve target recognition performance.

Key words: ship target recognition, high-resolution range profile (HRRP), multi-scale fusion kernel sparse preserving projection (MSFKSPP), feature extraction, dimensionality reduction

Xueling YANG, Gong ZHANG, Hu SONG. Ship recognition based on HRRP via multi-scale sparse preserving method[J]. Journal of Systems Engineering and Electronics, 2024, 35(3): 599-608.

Figures/Tables 7

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Target	Training sample	Testing sample
Class 1	700	500
Class 2	700	500
Class 3	700	500
Class 4	700	500

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Ship recognition based on HRRP via multi-scale sparse preserving method

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