In this paper a method capable of automatically classify radar signals of human hand-gestures exploiting the micro-Doppler signature is designed. In particular, the methodology focuses on the extraction of the Chebyshev moments from the cadence velocity diagram (CVD) of each recorded signal. The algorithm benefits from interesting properties of these moments such as the fact that they are defined on a discrete set and hence computed without approximations, as well as the symmetry property that allows to minimize the computational time. The experiments computed on the challenging real-recorded DopNet dataset show interesting results that confirm the effectiveness of the approach.
Pallotta, L., Cauli, M., Clemente, C., Fioranelli, F., Giunta, G., Farina, A. (2021). Classification of micro-Doppler radar hand-gesture signatures by means of Chebyshev moments. In 2021 IEEE International Workshop on Metrology for AeroSpace, MetroAeroSpace 2021 - Proceedings (pp.182-187). Institute of Electrical and Electronics Engineers Inc. [10.1109/MetroAeroSpace51421.2021.9511751].
Classification of micro-Doppler radar hand-gesture signatures by means of Chebyshev moments
Pallotta L.;Cauli M.;Giunta G.;
2021-01-01
Abstract
In this paper a method capable of automatically classify radar signals of human hand-gestures exploiting the micro-Doppler signature is designed. In particular, the methodology focuses on the extraction of the Chebyshev moments from the cadence velocity diagram (CVD) of each recorded signal. The algorithm benefits from interesting properties of these moments such as the fact that they are defined on a discrete set and hence computed without approximations, as well as the symmetry property that allows to minimize the computational time. The experiments computed on the challenging real-recorded DopNet dataset show interesting results that confirm the effectiveness of the approach.File | Dimensione | Formato | |
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