Mutual Information of Features Extracted from Human Micro-Doppler

Date

2013-01-01

Author

Tekeli, Burkan
Gurbuz, Sevgi Zubeyde
Yüksel Turgut, Ayşe Melda

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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The unique, bi-pedal motion of humans has been shown to generate a characteristic micro-Doppler signature in the time-frequency domain that can be used to discriminate humans from not just other targets, but also between different activities, such as walking and running. In the literature, many different features have been proposed for classification applications. However, it is not known which features have a greater impact on classification performance, or indeed how many features should be used to achieve good classification. In this work, the mutual information of features extracted from human micro-Doppler signatures is computed. Taking the problem of classifying human arm-swing as an example, the features extracted are ordered in terms of importance.

Subject Keywords

human classification, micro-Doppler, feature extraction, information theory I, RADAR, SIGNATURES, MODEL

URI

https://hdl.handle.net/11511/96470

DOI

https://doi.org/10.1109/siu.2013.6531440

Conference Name

21st Signal Processing and Communications Applications Conference (SIU)

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar

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Citation Formats

B. Tekeli, S. Z. Gurbuz, and A. M. Yüksel Turgut, “Mutual Information of Features Extracted from Human Micro-Doppler,” presented at the 21st Signal Processing and Communications Applications Conference (SIU), CYPRUS, 2013, Accessed: 00, 2022. [Online]. Available: https://hdl.handle.net/11511/96470.