Sigma-point Kalman filters for integrated navigation

Proceedings of the Annual Meeting - Institute of Navigation

Volumn , Issue , 2004, Pages 641-654

  Van Der Merwe, Rudolph ^a   Wan, Eric A ^a  

^a OREGON HEALTH AND SCIENCE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EXTENDED KALMAN FILTER (EKF); GAUSSIAN RANDOM VARIABLE (GRV); INERTIAL MEASUREMENT UNITS (IMU); NOISE VECTORS;

ACCELEROMETERS; GLOBAL POSITIONING SYSTEM; MATHEMATICAL MODELS; PATTERN RECOGNITION; RANDOM PROCESSES; SENSORS; SIGNAL PROCESSING; SIGNAL RECEIVERS; SIGNAL TO NOISE RATIO; VELOCITY MEASUREMENT;

KALMAN FILTERING;

EID: 8344263003     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (25)

1. A. A. Bogdanov and E. A. Wan. SDRE Control with Nonlinear Feedforward Compensation for a Small Unmanned Helicopter. In Proceedings of the 2nd AIM Unmanned Unlimited Systems, Technologies, and Operations Conference, San Diego, CA, September 2003. AIAA Paper Number: 2003-6512.
2. A. A. Bogdanov, E. A. Wan, M. Carlsson, R. Kieburtz, G. Harvey, J. Hunt, and R. van der Merwe. State-Dependent Ricatti Equation Control of a Small Unmanned Helicopter. In Proceedings of the AIAA Guidance Navigation and Control Conference, Austin, TX, August 2003. AIAA Paper Number: 2003-5672.
3. E. Feron. Aerial robotics project: Laboratory for information and decision systems mit. http://gewurtz.mit.edu/ research/heli.htm.
4. V. Gavrilets. Autonomous Acrobatic Maneuvering of Miniature Helicopters: Modeling and Control. PhD thesis, Massachusetts Institute of Technology, Cambridge, MA, 2003.
5. G. H. Golub and van Loan C. F. Matrix Computations. Johns Hopkins University Press, 1996.
6. M. Grewal, L. R. Weil, and A. P. Andrews. Global Positioning Systems, Inertial Navigation, and Integration. John Wiley & Sons, 1 edition, 2001.
7. S. Haykin, editor. Kalman Filtering and Neural Networks, chapter 7 - The Unscented Kalman Filter, E. A. Wan and R. van der Merwe, pages 221-280. Adaptive and Learning Systems for Signal Processing, Communications, and Control. Wiley, 2001.
8. Kazufumi Ito and Kaiqi Xiong. Gaussian Filters for Nonlinear Filtering Problems. IEEE Transactions on Automatic Control, 45(5):910-927, May 2000.
9. S. Julier, J. Uhlmann, and H. Durrant-Whyte. A new approach for filtering nonlinear systems. In Proceedings of the American Control Conference, pages 1628-1632, 1995.
10. S. J. Julier and J. K. Uhlmann. Unscented Filtering and Nonlinear Estimation. Proceedings of the IEEE, 92(3):401-422, March 2004.
11. T. D. Larsen, N. A. Andersen, and O. Ravn. Incorporation of Time Delayed Measurements in a Discrete-time Kalman Filter. In Proceedings of the 37th IEEE Conference on Decision & Control, pages 3972-3977, Tampa, Florida, USA, Dec 1998.
12. Frank L. Lewis. Optimal Estimation. John Wiley & Sons, Inc., New York, 1986.
13. S. Nassar, K. Schwarz, and N. El-Sheimy. INS and INS/GPS Accuracy Improvement Using Autoregressive (AR) Modeling of INS Sensor Errors. In Proceedings of ION-NTM, pages 936-944, San Diego, Jan 2004.
14. M. Norgaard, N. Poulsen, and O. Ravn. New Developments in State Estimation for Nonlinear Systems. Automatica, 36(11):1627-1638, November 2000.
15. J. M. Rolfe and K. J. Staples. Flight Simulation. Cambridge University Press, United Kingdom, 1986.
16. P.G. Savage. Strapdown Inertial Navigation Integration Algorithm Design Part 1: Attitude Algorithms. Journal of Guidance, Control, and Dynamics, 21(1):19-28, Jan 1998.
17. P.O. Savage. Strapdown Inertial Navigation Integration Algorithm Design Part 2: Velocity and Position Algorithms. Journal of Guidance, Control, and Dynamics, 21(2):208-221, March 1998.
18. Eun-Hwan Shin and Naser El-Sheimy. An Unscented Kalman Filter for In-Motion Alignment of Low-Cost IMUs. In Proceedings of the IEEE Frames Conference, pages 273-279, Monterey, California, April 2004.
19. B. Stevens and F. Lewis. Aircraft Control and Simulation. Wiley, New York, NY, 1992.
20. R. van der Merwe. Sigma-Point Kalman Filters for Probabilistic Inference in Dynamic State-Space Models. In Workshop on Advances in Machine Learning, Montreal, June 2003. http://www.iro. umontreal.ca/~kegl/CRMWorkshop/program. html.
21. R. van der Merwe. Sigma-Point Kalman Filters for Probabilistic Inference in Dynamic State-Space Models. PhD thesis, OGI School of Science & Engineering at Oregon Health & Science University, Portland, OR, April 2004.
22. R. van der Merwe and E. Wan. Efficient Derivative-Free Kalman Filters for Online Learning. In Proceedings of the 9th European Symposium on Artificial Neural Networks (ESANN), pages 205-210, Bruges, Belgium, Apr 2001.
23. R. van der Merwe and E. Wan. The Square-Root Unscented Kalman Filter for State- and Parameter-Estimation. In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), volume 6, pages 3461-3464, Salt Lake City, UT, May 2001.
24. R. van der Merwe, E. Wan, and S. J. Julier. Sigma-Point Kalman Filters for Nonlinear Estimation and Sensor-Fusion: Applications to Integrated Navigation. In Proceedings of the AIAA Guidance, Navigation & Control Conference, Providence, RI, Aug 2004.
25. E. A. Wan and R. van der Merwe. The Unscented Kalman Filter for Nonlinear Estimation. In Proceedings of IEEE Symposium on Adaptive Systems for Signal Processing Communications and Control (AS-SPCC), pages 153-158, Lake Louise, Alberta, Canada, October 2000.