Data Fusion with two Nonlinear Constraints on Kalman Filtering

W. Gao, J. Li, F. Yu, G. Zhou, C. Yu, M. Lin

Abstract: In this paper we investigate an integrated pedestrian navigation scenario to fuse data from three systems whose relative locations are known previously. In this excogitation a pedestrian is equipped with a GNSS (Global Navigation Satellite System) receiver on the shoulder and two MEMS (Micro Electro Mechanical Systems)-based IMU on the tiptoe and heel of a shoe. Due to the physical space description of the three systems two constraints can be obtained. One is based on the fixed distance between two inertial systems and the other is with reference to the approximately range between GNSS and one of the two IMUs. The suggested information fusion method is expected to make use of Kalman Filtering with state constraint.
Published in: Proceedings of IEEE/ION PLANS 2014
May 5 - 8, 2014
Hyatt Regency Hotel
Monterey, CA
Pages: 524 - 528
Cite this article: Gao, W., Li, J., Yu, F., Zhou, G., Yu, C., Lin, M., "Data Fusion with two Nonlinear Constraints on Kalman Filtering," Proceedings of IEEE/ION PLANS 2014, Monterey, CA, May 2014, pp. 524-528.
Full Paper: ION Members/Non-Members: 1 Download Credit
Sign In