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".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Nesta carta, apresentamos uma estimativa de orientação em tempo real e um esquema de rastreamento de movimento usando o método de filtragem de Kalman baseado em modelo múltiplo interativo (IMM). Dois filtros não lineares, filtro de Kalman estendido baseado em quaternion (QBEKF) e filtro de Kalman estendido baseado em giroscópio (GBEKF) são utilizados no estimador de orientação baseado em IMM proposto para estimativa do estado de movimento do sensor. No QBEKF são processadas medições de giroscópio, acelerômetro e magnetômetro; enquanto no GBEKF, as medições exclusivas do giroscópio são processadas. O algoritmo de modelo múltiplo interativo é usado para fundir os estados estimados por meio de ponderação de modelo adaptativo. Os resultados da simulação validam o conceito de projeto proposto, e o esquema é capaz de reduzir erros gerais de estimativa no rastreamento de movimento do sensor.
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Chin-Der WANN, Jian-Hau GAO, "Orientation Estimation for Sensor Motion Tracking Using Interacting Multiple Model Filter" in IEICE TRANSACTIONS on Fundamentals,
vol. E93-A, no. 8, pp. 1565-1568, August 2010, doi: 10.1587/transfun.E93.A.1565.
Abstract: In this letter, we present a real-time orientation estimation and motion tracking scheme using interacting multiple model (IMM) based Kalman filtering method. Two nonlinear filters, quaternion-based extended Kalman filter (QBEKF) and gyroscope-based extended Kalman filter (GBEKF) are utilized in the proposed IMM-based orientation estimator for sensor motion state estimation. In the QBEKF, measurements from gyroscope, accelerometer and magnetometer are processed; while in the GBEKF, sole measurements from gyroscope are processed. The interacting multiple model algorithm is used for fusing the estimated states via adaptive model weighting. Simulation results validate the proposed design concept, and the scheme is capable of reducing overall estimation errors in sensor motion tracking.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E93.A.1565/_p
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@ARTICLE{e93-a_8_1565,
author={Chin-Der WANN, Jian-Hau GAO, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Orientation Estimation for Sensor Motion Tracking Using Interacting Multiple Model Filter},
year={2010},
volume={E93-A},
number={8},
pages={1565-1568},
abstract={In this letter, we present a real-time orientation estimation and motion tracking scheme using interacting multiple model (IMM) based Kalman filtering method. Two nonlinear filters, quaternion-based extended Kalman filter (QBEKF) and gyroscope-based extended Kalman filter (GBEKF) are utilized in the proposed IMM-based orientation estimator for sensor motion state estimation. In the QBEKF, measurements from gyroscope, accelerometer and magnetometer are processed; while in the GBEKF, sole measurements from gyroscope are processed. The interacting multiple model algorithm is used for fusing the estimated states via adaptive model weighting. Simulation results validate the proposed design concept, and the scheme is capable of reducing overall estimation errors in sensor motion tracking.},
keywords={},
doi={10.1587/transfun.E93.A.1565},
ISSN={1745-1337},
month={August},}
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TY - JOUR
TI - Orientation Estimation for Sensor Motion Tracking Using Interacting Multiple Model Filter
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1565
EP - 1568
AU - Chin-Der WANN
AU - Jian-Hau GAO
PY - 2010
DO - 10.1587/transfun.E93.A.1565
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E93-A
IS - 8
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - August 2010
AB - In this letter, we present a real-time orientation estimation and motion tracking scheme using interacting multiple model (IMM) based Kalman filtering method. Two nonlinear filters, quaternion-based extended Kalman filter (QBEKF) and gyroscope-based extended Kalman filter (GBEKF) are utilized in the proposed IMM-based orientation estimator for sensor motion state estimation. In the QBEKF, measurements from gyroscope, accelerometer and magnetometer are processed; while in the GBEKF, sole measurements from gyroscope are processed. The interacting multiple model algorithm is used for fusing the estimated states via adaptive model weighting. Simulation results validate the proposed design concept, and the scheme is capable of reducing overall estimation errors in sensor motion tracking.
ER -