The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Este artigo apresenta um método que identifica a atividade humana a partir das informações de altura e seção transversal do radar Doppler (RCS) detectadas pelo radar MIMO (Multiple-Input Multiple-Output). Este método estima a localização tridimensional do alvo aplicando o método MUltiple SIgnal Classification (MUSIC) ao canal MIMO observado; o Doppler RCS é calculado a partir do sinal refletido do alvo. Um algoritmo de reconhecimento de gestos é aplicado à trajetória da transição temporal da altura humana estimada e ao Doppler RCS. Em experimentos, o método proposto atinge uma taxa de reconhecimento superior a 90% (média).
Dai SASAKAWA
Iwate University
Naoki HONMA
Iwate University
Takeshi NAKAYAMA
Panasonic Corporation
Shoichi IIZUKA
Panasonic Corporation
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Dai SASAKAWA, Naoki HONMA, Takeshi NAKAYAMA, Shoichi IIZUKA, "Human Activity Identification by Height and Doppler RCS Information Detected by MIMO Radar" in IEICE TRANSACTIONS on Communications,
vol. E102-B, no. 7, pp. 1270-1278, July 2019, doi: 10.1587/transcom.2018ANP0004.
Abstract: This paper introduces a method that identifies human activity from the height and Doppler Radar Cross Section (RCS) information detected by Multiple-Input Multiple-Output (MIMO) radar. This method estimates the three-dimensional target location by applying the MUltiple SIgnal Classification (MUSIC) method to the observed MIMO channel; the Doppler RCS is calculated from the signal reflected from the target. A gesture recognition algorithm is applied to the trajectory of the temporal transition of the estimated human height and the Doppler RCS. In experiments, the proposed method achieves over 90% recognition rate (average).
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2018ANP0004/_p
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@ARTICLE{e102-b_7_1270,
author={Dai SASAKAWA, Naoki HONMA, Takeshi NAKAYAMA, Shoichi IIZUKA, },
journal={IEICE TRANSACTIONS on Communications},
title={Human Activity Identification by Height and Doppler RCS Information Detected by MIMO Radar},
year={2019},
volume={E102-B},
number={7},
pages={1270-1278},
abstract={This paper introduces a method that identifies human activity from the height and Doppler Radar Cross Section (RCS) information detected by Multiple-Input Multiple-Output (MIMO) radar. This method estimates the three-dimensional target location by applying the MUltiple SIgnal Classification (MUSIC) method to the observed MIMO channel; the Doppler RCS is calculated from the signal reflected from the target. A gesture recognition algorithm is applied to the trajectory of the temporal transition of the estimated human height and the Doppler RCS. In experiments, the proposed method achieves over 90% recognition rate (average).},
keywords={},
doi={10.1587/transcom.2018ANP0004},
ISSN={1745-1345},
month={July},}
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TY - JOUR
TI - Human Activity Identification by Height and Doppler RCS Information Detected by MIMO Radar
T2 - IEICE TRANSACTIONS on Communications
SP - 1270
EP - 1278
AU - Dai SASAKAWA
AU - Naoki HONMA
AU - Takeshi NAKAYAMA
AU - Shoichi IIZUKA
PY - 2019
DO - 10.1587/transcom.2018ANP0004
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E102-B
IS - 7
JA - IEICE TRANSACTIONS on Communications
Y1 - July 2019
AB - This paper introduces a method that identifies human activity from the height and Doppler Radar Cross Section (RCS) information detected by Multiple-Input Multiple-Output (MIMO) radar. This method estimates the three-dimensional target location by applying the MUltiple SIgnal Classification (MUSIC) method to the observed MIMO channel; the Doppler RCS is calculated from the signal reflected from the target. A gesture recognition algorithm is applied to the trajectory of the temporal transition of the estimated human height and the Doppler RCS. In experiments, the proposed method achieves over 90% recognition rate (average).
ER -