다중 IMU 센서와 연합형 필터 융합 기반의 GPS-free 위치 추정 = GPS-free Localization Based on Multiple IMUs and Federated Filter Fusion

다국어 초록 (Multilingual Abstract)

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.

다국어 초록 (Multilingual Abstract)

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참고문헌 (Reference)

1 김응주, "항법시스템 신뢰성 향상을 위한 연합형 칼만필터 기반 이중 고장검출 알고리즘" 제어·로봇·시스템학회 26 (26): 136-143, 2020

2 한진수, "상용 MEMS IMU와 차량 CAN 데이터를 이용한 지상차량 항법용 GPS/INS/DR 통합항법시스템 구현 및 실측결과" 제어·로봇·시스템학회 26 (26): 55-65, 2020

3 J. B. Bancroft, "Twin IMU-HSGPS integration for pedestrian navigation" 16-20, 2008

4 I. Colomina, "Redundant IMUs for precise trajectory determination" 2004

5 A. M. Sabatini, "Quaternion-based extended Kalman filter for determining orientation by inertial and magnetic sensing" 53 (53): 2006

6 P. D. Groves, "Principles of GNSS, inertial, and multisensor integrated navigation systems" Artech House 2013

7 D. Choukroun, "Novel methods for attitude determination using vector observations" Technion-Israel Institute of Technology 2003

8 A. Waegli, "Noise reduction and estimation in multiple micro-electro-mechanical inertial systems" 21 : 1-11, 2010

9 M. A. Sturza, "Navigation system integrity monitoring using redundant measurements" 35 : 483-501, 1988

10 J. B. Bancroft, "Multiple inertial measurement unit integration for pedestrian navigation" University of Calgary 2010

11 M. Rhudy, "Fusion of GPS and redundant IMU data for attitude estimation" 2012

12 R. Mu, "Federated filter with strengthened FDIR capability for multiple sensor navigation system" 600-604, 2006

13 N. A. Carlson, "Federated filter for fault-tolerant integrated navigation systems" 110-119, 1988

14 N. A. Carlson, "Federated filter for distributed navigation and tracking applications" 340-353, 2002

15 S. O. H. Madgwick, "Estimation of IMU and MARG orientation using a gradient descent algorithm" 179-185, 2011

16 J. B. Bancroft, "Data fusion algorithms for multiple inertial measurement units" 11 (11): 6771-6798, 2001

17 J. L. Marins, "An extended Kalman filter for quaternion-based orientation estimation using MARG sensors" 2003-2011, 2001

18 M. Givens, "An estimation-domain approach to MEMS multi-IMU fusion for sUAS" 1048-1053, 2019

19 R. E. Kalman, "A new approach to linear filtering and prediction problems" 82 (82): 34-45, 1960

20 S. Sukkarieh, "A low-cost redundant inertial measurement unit for unmanned air vehicles" 19 : 1089-1103, 2000

21 E. Choi, "A consumer tracking estimator for vehicles in GPS-free environments" 63 (63): 450-458, 2017