MEMS based pedestrian navigation system

Journal of Navigation

Volumn 59, Issue 1, 2006, Pages 135-153

  Cho, Seong Yun ^a   Park, Chan Gook ^b  

^a Electronics and Telecommunications Research Institute (ETRI)   (South Korea)

^b Seoul National University   (South Korea)

Author keywords

Modified RHKF filter; Pedestrian navigation system

Indexed keywords

ACCELEROMETERS; ALGORITHMS; FIR FILTERS; KALMAN FILTERING; MAGNETIC FIELDS; MICROELECTROMECHANICAL DEVICES; NAVIGATION SYSTEMS; NEURAL NETWORKS; PERFORMANCE; TRACKING (POSITION);

NAVIGATION AID; PEDESTRIAN;

EID: 31144461059     PISSN: 03734633     EISSN: 14697785     Source Type: Journal    
DOI: 10.1017/S0373463305003486     Document Type: Article

References (23)

1. Animian, K., Ph. Robert, Jequier, E., and Schutz, Y. (1995). Estimation of Speed and Incline of Walking Using Neural Network. IEEE Trans. Instrumentation and Measurement.
2. Brown, R. G. and Hwang, P. Y. (1997). Introduction to Random Signals and Applied Kalman Filtering. Wiley.
3. Caruso, M. J. (1997). Applications of Magnetoresistive Sensors in Navigation Systems. Sensors and Actuators 1997.
4. Cho, S. Y. (2004). Design of a Pedestrian Navigation System and the Error Compensation Using RHEKF Filter. Ph.D. Dissertation, Department of Control and Instrumentation Engineering, Kwangwoon University, Korea.
5. Cho, S. Y. and Park, C. G. (2003). Tilt compensation algorithm for 2-axis magnetic compass. IEE Electronics Letters, 39.
6. Cho, S. Y., Park, C. G., and Jee, G. I. (2002). Measurement System of Walking Distance Using Low-Cost Accelerometers. Proc, 4th ASCC.
7. Cho, S. Y., Park, C. G., and Lee, J. G. (2003). A Personal Navigation System Using Low-Cost MEMS/GPS/Fluxgate. ION 59th annual meeting.
8. Danyand, L. and Xuanhuang, L. (1994). Optimal State Estimation without the Requirement of A Priori Statistics Information of the Initial State. IEEE Trans. AC. 39.
9. Farrel, J. A. and Barth, M. (1999). The Global Positioning System and Inertial Navigation. McGraw-Hill.
10. Fyfe, K. R. (1999). Motion Analysis System. United State Patent, No. 5,955,667.
11. Gabaglio, V. (2001). Centralized Kalman Filter for Augmented GPS Pedestrian Navigation. Proc. ION GPS.
12. Grewal, M. S., Weill, L. R. and Andrews, A. P. (2001). Global Positioning Systems, Inertial Navigation, and Integration. John Wiley & Sons, Inc.
13. Jirawimut, R., Ptasinski, P., Garaj, V., Cecelja, F., and Balachandran, W. (2001) A Method for Dead Reckoning Parameter Correction in Pedestrian Navigation System. Proc. 18th IEEE Instrumentation and Measurement Technology Conf.
14. Kappi, J., Collin, J., Saarinen, J., and Syrjarinne, J. (2001). MEMS-IMU Based Pedestrian Navigator for Handheld Devices. Proc. ION GPS.
15. Kim, P. S. (2002). Separate-Bias Estimation Scheme with Diversely Behaved Biases. IEEE Trans. AES. 38.
16. Kwon, W. H., Kim, P. S. and Park, P. G. (1999). A Receding Horizon Kalman FIR Filter for Discrete Time-Invariant Systems. IEEE, Trans. AC, 44.
17. Ladetto, Q. (2000). On Foot Navigation: Continuous Step Calibration Using Both Complementary Recursive Prediction and Adaptive Kalman Filtering. Proc. ION GPS.
18. Lee, S. W. and Mase, K. (2001). Recognition of Walking Behaviors for Pedestrian Navigation. Proc. IEEE Int'l Conf. Control Applications.
19. Leppakoski, H., Kappi, J., Syrjarinne, J., and Takala, J. (2002). Error Analysis of Step Length Estimation Pedestrian Dead Reckoning. Proc. ION GPS.
20. Levi, R. W. and Judd, T. (1996). Dead Reckoning Navigational System Using Accelerometer to Measure Foot Impacts. United States Patent, No. 5,583,776.
21. Ling, K. V. and Lim, K. W. (1999). Receding Horizon Recursive State Estimation. IEEE Trans. AC, 44.
22. Parkinson, B. and Axelad, P. (1996). Global Positioning System: Theory and applications. American Institute of Aeronautics and Astronautics.
23. Sagawa, K., Susumago, M., and Inooka, M. (2001). Unrestricted Measurement Method of Three-dimensional Walking Distance Utilizing Body Acceleration and Terrestrial Magnetism. Proc. ICCAS.