Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1/W4, 75-81, 2015
http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-1-W4/75/2015/
doi:10.5194/isprsarchives-XL-1-W4-75-2015
© Author(s) 2015. This work is distributed
under the Creative Commons Attribution 3.0 License.
 
26 Aug 2015
A NEW SURVEY ON SELF-TUNING INTEGRATED LOW-COST GPS/INS VEHICLE NAVIGATION SYSTEM IN HARSH ENVIRONMENT
N. Navidi and R. Landry Jr. Département de Génie Électrique École de Technologie Supérieure (ETS), Montréal, Canada
Keywords: GPS/INS integration, Extended Kalman Filter, Fuzzy Inference System Abstract. Nowadays, Global Positioning System (GPS) receivers are aided by some complementary radio navigation systems and Inertial Navigation Systems (INS) to obtain more accuracy and robustness in land vehicular navigation. Extended Kalman Filter (EKF) is an acceptable conventional method to estimate the position, the velocity, and the attitude of the navigation system when INS measurements are fused with GPS data. However, the usage of the low-cost Inertial Measurement Units (IMUs) based on the Micro-Electro-Mechanical Systems (MEMS), for the land navigation systems, reduces the precision and stability of the navigation system due to their inherent errors. The main goal of this paper is to provide a new model for fusing low-cost IMU and GPS measurements. The proposed model is based on EKF aided by Fuzzy Inference Systems (FIS) as a promising method to solve the mentioned problems. This model considers the parameters of the measurement noise to adjust the measurement and noise process covariance. The simulation results show the efficiency of the proposed method to reduce the navigation system errors compared with EKF.
Conference paper (PDF, 1658 KB)


Citation: Navidi, N. and Landry Jr., R.: A NEW SURVEY ON SELF-TUNING INTEGRATED LOW-COST GPS/INS VEHICLE NAVIGATION SYSTEM IN HARSH ENVIRONMENT, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-1/W4, 75-81, doi:10.5194/isprsarchives-XL-1-W4-75-2015, 2015.

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