Assessment of the Integration Strategy between GPS and Body-Worn MEMS Sensors with Application to Sports

This paper describes experiments that were performed involving a professional downhill skier equipped with a low-cost L1 GPS receiver and a MEMS-IMU composed of 3 single axis gyroscopes, accelerometers and magnetometers. In addition, the skier carried an L1/L2 GPS receiver and a tactical-grade IMU (LN200). The experiments aimed to assess the navigation performance of different GPS/MEMS-IMU integration strategies compared to high-quality GPS/INS integration. After presenting an overview of currently applied integration methods, the unscented Kalman filter approach in loosely coupled mode. The relevance of the simple MEMS-IMU sensor error model was verified by comparing the filter output to the reference data.

[1]  K.P. Schwarz,et al.  Aided versus embedded-a comparison of two approaches to GPS/INS integration , 1994, Proceedings of 1994 IEEE Position, Location and Navigation Symposium - PLANS'94.

[2]  S. Julier,et al.  A General Method for Approximating Nonlinear Transformations of Probability Distributions , 1996 .

[3]  D. Gingras,et al.  Comparison between the unscented Kalman filter and the extended Kalman filter for the position estimation module of an integrated navigation information system , 2004, IEEE Intelligent Vehicles Symposium, 2004.

[4]  Mangesh Chansarkar Neural Networks in GPS Navigation , 2000, GPS Solutions.

[5]  Rudolph van der Merwe,et al.  Efficient derivative-free Kalman filters for online learning , 2001, ESANN.

[6]  Yuanxi Yang,et al.  An Optimal Adaptive Kalman Filter , 2006 .

[7]  Jan Skaloud,et al.  Assessment of GPS/MEMS-IMU Integration Performance in Ski Racing , 2007 .

[8]  Rudolph van der Merwe,et al.  The unscented Kalman filter for nonlinear estimation , 2000, Proceedings of the IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium (Cat. No.00EX373).

[9]  D K Smith,et al.  Numerical Optimization , 2001, J. Oper. Res. Soc..

[10]  N. El-Sheimy,et al.  Improvement of MEMS-IMU/GPS performance using fuzzy modeling , 2006 .

[11]  Aboelmagd Noureldin,et al.  Optimizing neuro-fuzzy modules for data fusion of vehicular navigation systems using temporal cross-validation , 2007, Eng. Appl. Artif. Intell..

[12]  John Weston,et al.  Strapdown Inertial Navigation Technology , 1997 .

[13]  Jonathan P. How,et al.  GPS estimation algorithms for precise velocity, slip and race-track position measurements , 2002 .

[14]  David Törnqvist,et al.  Statistical Fault Detection with Applications to IMU Disturbances , 2006 .

[15]  Jan Skaloud,et al.  Turning Point - Trajectory Analysis for Skiers , 2007 .

[16]  Yong Li,et al.  Low-cost tightly coupled GPS / INS integration based on a nonlinear Kalman filtering design , 2006 .

[17]  Chris Hide,et al.  Adaptive Kalman Filtering for Low-cost INS/GPS , 2002, Journal of Navigation.

[18]  I. Colomina,et al.  REDUNDANT IMUS FOR PRECISE TRAJECTORY DETERMINATION , 2004 .