@inproceedings{80d4e07a3aa54dd195f08585a3ab06b1,
title = "Classification of micro-Doppler radar hand-gesture signatures by means of Chebyshev moments",
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",
keywords = "micro-Doppler, hand-gesture recognition, image moments, millimeter wave, automatic target recognition (ATR)",
author = "Luca Pallotta and Michela Cauli and Carmine Clemente and Francesco Fioranelli and Gaetano Giunta and Alfonso Farina",
note = "{\textcopyright} 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.",
year = "2021",
month = aug,
day = "19",
doi = "10.1109/MetroAeroSpace51421.2021.9511751",
language = "English",
isbn = "9781728175577",
series = "IEEE International Workshop on Metrology for Aerospace",
publisher = "IEEE",
pages = "182--187",
booktitle = "2021 IEEE 8th International Workshop on Metrology for AeroSpace (MetroAeroSpace)",
}