Importance Ranking of Features for Human Micro-Doppler Classification with a Radar Network

Date

2013-01-01

Author

Gurbuz, Sevgi Zubeyde
Tekeli, Burkan
Yüksel Turgut, Ayşe Melda
Karabacak, Cesur
Gurbuz, Ali Cafer
Guldogan, Mehmet Burak

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Over the past decade, the human micro-Doppler signature has been a subject of intense research. In particular, much work has been done in relation to computing features for use in a variety of classification problems, such as arm swing detection, activity classification, and target identification. Although dozens of features have been proposed for these purposes, little work has examined the issue of which features are more important - i.e., have a greater impact on classification performance - than others. In this work, an information theoretic approach is applied to compute the importance ranking of features prior to classification for the specific problem of discriminating human walking from running. Results show that the ranking of features according to mutual information directly relates to classification performance using support vector machines.

Subject Keywords

human micro-Doppler, feature selection, classification, multistatic radar, radar network, SIGNATURES

URI

https://hdl.handle.net/11511/96500

Conference Name

16th International Conference on Information Fusion (FUSION)

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar

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Citation Formats

S. Z. Gurbuz, B. Tekeli, A. M. Yüksel Turgut, C. Karabacak, A. C. Gurbuz, and M. B. Guldogan, “Importance Ranking of Features for Human Micro-Doppler Classification with a Radar Network,” presented at the 16th International Conference on Information Fusion (FUSION), İstanbul, Türkiye, 2013, Accessed: 00, 2022. [Online]. Available: https://hdl.handle.net/11511/96500.