Uncertainty Propagation of correlated quaternion and Euclidean states using partially-conditioned Gaussian mixtures

The partially-conditioned Gaussian (PCG) density, a variant of the Gauss-Bingham density, quantifies the uncertainty of a state vector comprised of an attitude quaternion and other Euclidean states on their natural manifold, the unit hypercylinder. The conditioned Gaussian density is first developed by conditioning a Gaussian density on the unit hypersphere, and is an alternate representation of the Bingham density. The PCG density is then developed, which conditions only the quaternion portion of the aforementioned state vector on the unit hypersphere. The PCG density is then extended to the PCG mixture density, which can be used to approximate an arbitrary density on the unit hypercylinder. A method to construct a PCG mixture density approximating the PCG density on the two-dimensional cylinder is then developed. The temporal evolution of the PCG mixture density given system dynamics is then quantified and is compared to a Monte Carlo approach in order to verify its performance.

[1]  Kyle J. DeMars,et al.  Uncertainty Propagation of Correlated Quaternion and Euclidean States using the Gauss-Bingham Density , 2016 .

[2]  A. Wood,et al.  Saddlepoint approximations for the Bingham and Fisher–Bingham normalising constants , 2005 .

[3]  J. Junkins,et al.  Optimal Estimation of Dynamic Systems , 2004 .

[4]  Gerhard Kurz,et al.  Unscented Orientation Estimation Based on the Bingham Distribution , 2013, IEEE Transactions on Automatic Control.

[5]  Gerhard Kurz,et al.  A new probability distribution for simultaneous representation of uncertain position and orientation , 2014, 17th International Conference on Information Fusion (FUSION).

[6]  Gerhard Kurz,et al.  Recursive Bingham filter for directional estimation involving 180 degree symmetry , 2014 .

[7]  Tomonari Sei,et al.  Calculating the normalising constant of the Bingham distribution on the sphere using the holonomic gradient method , 2013, Stat. Comput..

[8]  John L. Crassidis,et al.  Fundamentals of Spacecraft Attitude Determination and Control , 2014 .

[9]  Y. Ho,et al.  A Bayesian approach to problems in stochastic estimation and control , 1964 .

[10]  H. Sorenson,et al.  Nonlinear Bayesian estimation using Gaussian sum approximations , 1972 .

[11]  Alan Edelman,et al.  The efficient evaluation of the hypergeometric function of a matrix argument , 2006, Math. Comput..

[12]  Gary R. Bradski,et al.  Monte Carlo Pose Estimation with Quaternion Kernels and the Bingham Distribution , 2011, Robotics: Science and Systems.

[13]  Christopher Bingham An Antipodally Symmetric Distribution on the Sphere , 1974 .

[14]  H. Sorenson,et al.  Recursive bayesian estimation using gaussian sums , 1971 .

[15]  Vivek Vittaldev,et al.  Collision probability for space objects using Gaussian mixture models , 2013 .

[16]  Gerhard Kurz,et al.  Efficient Bingham filtering based on saddlepoint approximations , 2014, 2014 International Conference on Multisensor Fusion and Information Integration for Intelligent Systems (MFI).

[17]  A. Jazwinski Stochastic Processes and Filtering Theory , 1970 .

[18]  Leslie Pack Kaelbling,et al.  Tracking 3-D Rotations with the Quaternion Bingham Filter , 2013 .