Data processing: vehicles – navigation – and relative location – Navigation – Employing position determining equipment
Reexamination Certificate
2007-10-30
2007-10-30
Zanelli, Michael J. (Department: 3661)
Data processing: vehicles, navigation, and relative location
Navigation
Employing position determining equipment
C701S220000, C701S200000, C342S357490
Reexamination Certificate
active
11099433
ABSTRACT:
A method of estimating the navigational state of a system entails acquiring observation data produced by noisy measurement sensors and providing a probabilistic inference system to combine the observation data with prediction values of the system state space model to estimate the navigational state of the system. The probabilistic inference system is implemented to include a realization of a Gaussian approximate random variable propagation technique performing deterministic sampling without analytic derivative calculations. This technique achieves for the navigational state of the system an estimation accuracy that is greater than that achievable with an extended Kalman filter-based probabilistic inference system.
REFERENCES:
patent: 5627768 (1997-05-01), Uhlmann et al.
patent: 5963888 (1999-10-01), Uhlmann et al.
patent: 6331835 (2001-12-01), Gustafson et al.
patent: 6408245 (2002-06-01), An et al.
patent: 6721657 (2004-04-01), Ford et al.
Bouvet et al.; Improving the accuracy of dynamic localization systems using RTK GPS by identifying GPS latency; Proceedings of the 2000 IEEE International Conf. on Robotics and Automation; Apr. 2000; pp. 2525-2530.
Simon J. Julier, Jeffrey K. Uhlmann, and Hugh F. Durrant-Whyte, Robotics Research Group, Dept. of Engineering Science, University of Oxford, Oxford, UK, “A New Approach for Filtering Nonlinear Systems,” 1995.
Eric A. Wan and Rudolph van der Merwe, Oregon Graduate Institute of Science & Technology, Beaverton, Oregon, USA, “The Unscented Kalman Filter for Nonlinear Estimation,” Oct. 2000.
Rudolph van der Merwe and Eric Wan, OGI School of Science and Engineering, Oregon Health & Science University, “Gaussian Mixture Sigma-Point Particle Filters for Sequential Probabilistic Inference in Dynamic State-Space Models,” Apr. 2003.
Rudolph van der Merwe and Eric A. Wan, Oregon Graduate Institute of Science and Technology,“The Square Root Unscented Kalman Filter for State and Parameter-Estimation,” May 2001.
Dr. Michael K. Martin and Bruce C. Detterich, “C-MIGITS™ II Design and Performance,” The Satellite Division of The Institute of Navigation, Sep. 1997, pp. 1-8.
John L. Crassidis and F. Landis Markley,“Unscented Filtering for Spacecraft Attitude Estimation,”Journal of Guidance, Control, and Dynamics, vol. 26, No. 4, Jul.-Aug. 2003, pp. 536-542.
Derek B. Kingston, Randal W. Beard, “Real-Time Attitude and Position Estimation for Small UAVs,” 2004 IEEE American Control Conference.
Matthew D. Lichter and Steven Dubowsky, Department of Mechanical Engineering, Massachusetts Institute of Technology, “Estimation of state, shape, and inertial parameters of space objects from sequences of range images,” Oct. 2003.
Marius Niculescu, Aerosonde Robotic Aircraft, Sensor Fusion Algorithms for Unmanned Air Vehicles, Nov. 13, 2001, pp. 1-8.
Julier Simon J.
van der Merwe Rudolph
Wan Eric A.
Oregon Health & Science University
Stoel Rives LLP
LandOfFree
Navigation system applications of sigma-point Kalman filters... does not yet have a rating. At this time, there are no reviews or comments for this patent.
If you have personal experience with Navigation system applications of sigma-point Kalman filters..., we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Navigation system applications of sigma-point Kalman filters... will most certainly appreciate the feedback.
Profile ID: LFUS-PAI-O-3886771