TY - JOUR
T1 - Gps-free localization based on multiple imus and federated filter fusion
AU - Kim, Jiyeon
AU - Song, Moogeun
AU - Kim, Jaehoon
AU - Lee, Dongik
N1 - Publisher Copyright:
© ICROS 2020.
PY - 2020
Y1 - 2020
N2 - Localization techniques based on the integration of MEMSs(Micro Electro Mechanical Systems) and GPS(Global Positioning System) have been widely used in various fields, including aerospace, biomedical, automotive, and robotics, among others. However, it is well known that GPS signals are weakened or blocked in tunnels, urban canyons, or indoor environments. A solution is to use multiple IMU(Inertial Measurement Unit) sensors. In this paper, we present a federated fusion algorithm for GPS-free localization using multiple MEMS IMUs. The federated filter uses a master fusion algorithm to combine the outputs of independent sensors so that the current position can be estimated. The proposed structure exploits a quaternion-based extended Kalman filter and a linear Kalman filter for attitude estimations and velocity estimations, respectively. The effectiveness of the proposed algorithm was demonstrated with MATLAB simulation.
AB - Localization techniques based on the integration of MEMSs(Micro Electro Mechanical Systems) and GPS(Global Positioning System) have been widely used in various fields, including aerospace, biomedical, automotive, and robotics, among others. However, it is well known that GPS signals are weakened or blocked in tunnels, urban canyons, or indoor environments. A solution is to use multiple IMU(Inertial Measurement Unit) sensors. In this paper, we present a federated fusion algorithm for GPS-free localization using multiple MEMS IMUs. The federated filter uses a master fusion algorithm to combine the outputs of independent sensors so that the current position can be estimated. The proposed structure exploits a quaternion-based extended Kalman filter and a linear Kalman filter for attitude estimations and velocity estimations, respectively. The effectiveness of the proposed algorithm was demonstrated with MATLAB simulation.
KW - Extended Kalman filter
KW - Federated filter
KW - Multiple IMUs
KW - Position estimation
KW - Quaternion
UR - http://www.scopus.com/inward/record.url?scp=85091720969&partnerID=8YFLogxK
U2 - 10.5302/J.ICROS.2020.20.0069
DO - 10.5302/J.ICROS.2020.20.0069
M3 - Article
AN - SCOPUS:85091720969
SN - 1976-5622
VL - 26
SP - 708
EP - 714
JO - Journal of Institute of Control, Robotics and Systems
JF - Journal of Institute of Control, Robotics and Systems
IS - 9
ER -