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University of Colorado Boulder ASEN 5070: Statistical Orbit Determination I Fall 2014 Professor Brandon A. Jones Lecture 34: Probability Ellipsoids
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University of Colorado Boulder Homework 10 due on Friday Lecture quiz due by 5pm on Friday ◦ Posted later today Guest Lecture on Wednesday No lecture on Friday ◦ Make-up nominally posted over the break 2
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University of Colorado Boulder 3 Probability Ellipsoids
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University of Colorado Boulder An “ellipsoid” is an n-dimensional ellipse or, more generally, a hyperellipsoid. P, the variance-covariance matrix, represents the uncertainty in the state estimate. The truth is that there is a relationship between the variances in each component, and visualization of such relationships provides information on quality of the state 5
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University of Colorado Boulder Generally the best to represent the probability ellipsoid using the covariance matrix’s principal axes. 6
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University of Colorado Boulder For a filter estimated Cartesian state, it is easier to use the principal axes of P to construct the probability ellipsoid For this we use the eigenvector/value decomposition: 8
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University of Colorado Boulder The matrix U may be used to diagonalize P ◦ Called a principal axis transformation Any realization of the random vector x may be rotated into the principal axes via 9
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University of Colorado Boulder This is really useful, because if P is oriented in Cartesian coordinates, we don’t really know what the size of a probability ellipsoid is. 10 ???
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University of Colorado Boulder The axis sizes of the l σ ellipsoid are then 13 The orientation of the ellipsoids is determined by the eigenvector matrix U. We may interpret this matrix as an Euler angle sequence of rotations:
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University of Colorado Boulder 16 The Euler angles are defined by:
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University of Colorado Boulder Although the Euler-rotation interpretation provides some understanding of the probability ellipsoid, we do not have to compute the angles ◦ The eigenvector matrix U defines the transformation from the frame used to define P and the principal axis frame 17
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University of Colorado Boulder Consider the case: 18 We will plot this case in MATLAB using:
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University of Colorado Boulder 19 Views of Error Ellipsoid view (0,0)* azimuth =0, elevation =0 view down the negative y-axis *view(azimuth, elevation), azimuth is a clockwise rotation about the positive z-axis
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University of Colorado Boulder 20 Views of Error Ellipsoid view (90°,0) view down the positive x-axis
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University of Colorado Boulder 21 Views of Error Ellipsoid view (0,90°) view down the positive z-axis
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University of Colorado Boulder 22 Views of Error Ellipsoid view (-37.5°,0) standard matlab view
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University of Colorado Boulder Probability of being within 1σ, 2σ, and 3σ differs based on dimension ◦ Univariate: 1σ – 0.683 2σ – 0.954 3σ – 0.997 ◦ Trivariate 1σ – 0.200 2σ – 0.739 3σ – 0.971 23
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University of Colorado Boulder Output of eig() (in almost any language) is not always consistent with the presented formulation ◦ Always inspect the covariance matrix and compare it to the plotted ellipsoid ◦ For example, does the standard deviation in the x- component in P match the plot 24
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University of Colorado Boulder Problem first identified in 1996: ◦ Junkins, et al., “Non-Gaussian Error Propagation in Orbital Mechanics”, Journal of Astronautical Sciences, V. 44, N. 4, 1996 pp. 541-563 Multiple methods exists for nonlinear propagation: ◦ Monte Carlo ◦ State transition tensors (STT) ◦ Gaussian Mixtures ◦ Polynomial Chaos 27
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University of Colorado Boulder The STM represents a 2 nd -order tensor ◦ Generated via the first derivative of the force model Accuracy improved with the inclusion of higher-order effects A STT maintains higher order derivatives for mapping of the a priori p.d.f. 28
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University of Colorado Boulder 29 Fujimoto, et al., 2011
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University of Colorado Boulder 30 Horwood, et al., JGCD, Nov.-Dec., 2011
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University of Colorado Boulder Based on Weiner’s Homogeneous Chaos (1938) Generates an approximate solution to a stochastic ODE: 31 More commonly used is structures, CFD, applied physics, and other fields We are applying it to orbital mechanics
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University of Colorado Boulder 32 Use polynomial surrogate to approximate the p.d.f. PC requires ~100-200 ODE evaluations Monte Carlo requires more than 100,000 evaluations Image: Jones, et al., 2013
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University of Colorado Boulder 33 Part of NASA/GSFC-based navigation team for the Magnetospheric Multi-Scale (MMS) mission ◦ Leveraging CU-developed methods and applications of uncertainty quantification ◦ Applying polynomial chaos (PC) to the estimation of collision probabilities ◦ Includes post-maneuver uncertainty quantification Collision Risk Relative Accuracy
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