Hybrid extended particle filter (HEPF) for integrated inertial navigation and global positioning systems

Measurement Science and Technology

Volumn 20, Issue 5, 2009, Pages

  Aggarwal, Priyanka ^a   Syed, Zainab ^a   El Sheimy, Naser ^a  

^a UNIVERSITY OF CALGARY   (Canada)

Author keywords

INS GPS integration; Low cost MEMS sensors; Particle filter

Indexed keywords

DOUBLE-INTEGRATION; GAUSSIAN NOISE DISTRIBUTIONS; HIGHLY NONLINEAR; INERTIAL NAVIGATIONS; INS/GPS INTEGRATION; INTEGRATED NAVIGATION SYSTEMS; LAND VEHICLE NAVIGATION; LOW-COST MEMS SENSORS; MEASUREMENT MODEL; MICROELECTROMECHANICAL SYSTEM SENSORS; MOVING OBJECTS; NAVIGATION DATA; NON-GAUSSIAN NOISE; NON-LINEARITY; PARTICLE FILTER; TIME VARYING; VEHICLE DYNAMICS;

AIR FILTERS; CELLULAR RADIO SYSTEMS; COMPOSITE MICROMECHANICS; CONTROL THEORY; EXTENDED KALMAN FILTERS; GAUSSIAN NOISE (ELECTRONIC); GLOBAL POSITIONING SYSTEM; INERTIAL NAVIGATION SYSTEMS; MEMS; MICROELECTROMECHANICAL DEVICES; MICROSENSORS; NONLINEAR EQUATIONS; NONLINEAR FILTERING; NONLINEAR SYSTEMS; OUTAGES; TRACKING (POSITION);

NAVIGATION;

EID: 70349326003     PISSN: 09570233     EISSN: 13616501     Source Type: Journal    
DOI: 10.1088/0957-0233/20/5/055203     Document Type: Article

References (18)

1. Haug A J 2005 A Tutorial on Bayesian Estimation and Tracking Techniques Applicable to Nonlinear and Non-Gaussian Processes (USA: MITRE Corporation)
2. Ristic B, Arulampalan S and Gordon N 2004 Beyond the Kalman Filter: Particle Filters for Tracking Applications (Boston, MA: Artech House)
3. Farrell J A and Barth M 1999 The Global Positioning System and Inertial Navigation (New York: McGraw-Hill)
4. Schwarz K P and Wei M 1994 Modeling INS/GPS for attitude and gravity applications Proc. High Precision Navigation 95 pp 200-18
5. Aggarwal P, Gu D and El-Sheimy N 2007 Extended particle filter (EPF) for land vehicle navigation applications Proc. Int. Global Navigation Satellite Systems (4-6 December, Sydney, Australia)
6. Shin E H 2005 Estimation techniques for low-cost inertial navigation PhD Thesis University of Calgary, Canada UCGE Report 20219
7. Grewal M S and Andrews A P 2001 Kalman Filtering: Theory and Practice using Matlab (New York: Wiley)
8. Doucet A, Godsill S J and Andrieu C 2000 On sequential simulation-based methods for Bayesian filtering Stat. Comput. 10 197-208
9. Doucet A, De Freitas N and Gordon N 2001 Sequential Monte Carlo Methods in Practice (New York: Springer)
10. Gordon N, Salmond D J and Smith A F M 1993 Novel approach to nonlinear/non-Gaussian Bayesian state estimation Proc. Radar and Signal Processing, IEE Proc. F vol 140 pp 107-13
11. Bergman N 1999 Recursive Bayesian estimation: navigation and tracking applications Dissertation No. 579, Linköping University, Sweden
12. Arulampalan S, Maksell S, Gordon N and Clapp T 2002 A tutorial of particle filters for online nonlinear/non-Gaussian Bayesian tracking IEEE Trans. Signal Process. 50 174-88
13. Van Der Merwe R, Doucet A, De Freitas N and Wan E 2000 The unscented particle filter Advances in Neural Information Processing Systems (NIPS13) pp 584-90
14. Nordlund P J 2002 Sequential Monte Carlo filters and integrated navigation Thesis no. 945, Linköping University, Linköping, Sweden
15. Andrieu C, Doucet A and Fitzgerald W J 2001 An introduction to Monte Carlo methods for Bayesian data analysis Nonlinear Dynamics and Statistics ed A I Mees (Boston, MA: Birkhäuser) pp 169-217
16. Pitt M K and Shephard N 1999 Filtering via simulation: auxiliary particle filters J. Am. Stat. Assoc. 94 590-9
17. Liu J and Chen R 1998 Sequential Monte Carlo methods for dynamics systems J. Am. Stat. Assoc. 93 1031-41
18. Shin E H and El-Sheimy N 2005 Aided Inertial Navigation System (AINS) Toolbox for MatLab Software (Canada: University of Calgary)