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Proceedings Paper

The optional selection of micro-motion feature based on Support Vector Machine
Author(s): Bo Li; Hongmei Ren; Zhi-he Xiao; Jing Sheng
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Paper Abstract

Micro-motion form of target is multiple, different micro-motion forms are apt to be modulated, which makes it difficult for feature extraction and recognition. Aiming at feature extraction of cone-shaped objects with different micro-motion forms, this paper proposes the best selection method of micro-motion feature based on support vector machine. After the time-frequency distribution of radar echoes, comparing the time-frequency spectrum of objects with different micro-motion forms, features are extracted based on the differences between the instantaneous frequency variations of different micro-motions. According to the methods based on SVM (Support Vector Machine) features are extracted, then the best features are acquired. Finally, the result shows the method proposed in this paper is feasible under the test condition of certain signal-to-noise ratio(SNR).

Paper Details

Date Published: 15 November 2017
PDF: 7 pages
Proc. SPIE 10605, LIDAR Imaging Detection and Target Recognition 2017, 106052X (15 November 2017); doi: 10.1117/12.2294493
Show Author Affiliations
Bo Li, Science and Technology on Electromagnetic Scattering Lab. (China)
Hongmei Ren, Science and Technology on Electromagnetic Scattering Lab. (China)
Zhi-he Xiao, Science and Technology on Electromagnetic Scattering Lab. (China)
Jing Sheng, Science and Technology on Electromagnetic Scattering Lab. (China)


Published in SPIE Proceedings Vol. 10605:
LIDAR Imaging Detection and Target Recognition 2017
Yueguang Lv; Weimin Bao; Weibiao Chen; Zelin Shi; Jianzhong Su; Jindong Fei; Wei Gong; Shensheng Han; Weiqi Jin; Jian Yang, Editor(s)

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