Satellite collision probability estimation using polynomial chaos expansions

Abstract This paper presents the application of polynomial chaos (PC) to estimating the probability of collision between two spacecraft. Common methods of quantifying this probability for conjunction assessment use either Monte Carlo analyses or include simplifying assumptions to improve tractability. A PC expansion, or PCE, provides a means for approximating the solution to a large set of stochastic ordinary differential equations, which includes orbit propagation. When compared to Monte Carlo methods, non-intrusive, i.e., sampling-based, PCE generation techniques may greatly reduce the number of orbit propagations required to approximate the possibly non-Gaussian, a posteriori probability density function. The presented PC-based method of computing collision probability requires no fundamental simplifying assumptions, and reduces the computation time compared to Monte Carlo. This paper considers two cases where the common conjunction assessment assumptions are no longer valid. The results indeed demonstrate a reduction in computation time when compared to Monte Carlo, and improved accuracy when compared to simplified techniques.

[1]  Salvatore Alfano Toroidal path filter for orbital conjunction screening , 2012 .

[2]  R. Ghanem,et al.  Stochastic Finite Elements: A Spectral Approach , 1990 .

[3]  Matthew M. Berry,et al.  Implementation of Gauss-Jackson Integration for Orbit Propagation , 2004 .

[4]  Salvatore Alfano Determining satellite close approaches, part 2 , 1994 .

[5]  Roger Ghanem,et al.  Stochastic Finite Element Analysis for Multiphase Flow in Heterogeneous Porous Media , 1998 .

[6]  R. Patera General Method for Calculating Satellite Collision Probability , 2001 .

[7]  Daniel J. Scheeres,et al.  Analytical Nonlinear Propagation of Uncertainty in the Two-Body Problem , 2012 .

[8]  D. Vose Risk Analysis: A Quantitative Guide , 2000 .

[9]  Donald W. Phillion,et al.  Monte Carlo Method for Collision Probability Calculations Using 3D Satellite Models , 2010 .

[10]  S. Alfano,et al.  Satellite Conjunction Monte Carlo Analysis , 2009 .

[11]  J. Jackson Note on the Numerical Integration of $\frac{{d}^{2}x}{{dt}^{2}}=f(x,t)$ , 1924 .

[12]  Salvatore Alfano,et al.  Determining If Two Solid Ellipsoids Intersect , 2003 .

[13]  O. L. Maître,et al.  Spectral Methods for Uncertainty Quantification: With Applications to Computational Fluid Dynamics , 2010 .

[14]  A. Morselli,et al.  Rigorous computation of orbital conjunctions , 2012 .

[15]  Jing Li,et al.  An efficient surrogate-based method for computing rare failure probability , 2011, J. Comput. Phys..

[16]  J. Dormand,et al.  High order embedded Runge-Kutta formulae , 1981 .

[17]  Roger G. Ghanem,et al.  Physical Systems with Random Uncertainties: Chaos Representations with Arbitrary Probability Measure , 2005, SIAM J. Sci. Comput..

[18]  Russell P. Patera,et al.  Satellite Collision Probability for Nonlinear Relative Motion , 2002 .

[19]  Roger Ghanem,et al.  Ingredients for a general purpose stochastic finite elements implementation , 1999 .

[20]  Richard W. Ghrist,et al.  Impact of Non-Gaussian Error Volumes on Conjunction Assessment Risk Analysis , 2012 .

[21]  D. Xiu Fast numerical methods for stochastic computations: A review , 2009 .

[22]  D. Xiu,et al.  Modeling uncertainty in flow simulations via generalized polynomial chaos , 2003 .

[23]  Linda L. Crawford,et al.  An analytic method to determine future close approaches between satellites , 1984 .

[24]  Kyle J. DeMars,et al.  Orbit Determination Performance Improvements for High Area-to-Mass Ratio Space Object Tracking Using an Adaptive Gaussian Mixtures Estimation Algorithm , 2009 .

[25]  Roger Ghanem,et al.  Stochastic model reduction for chaos representations , 2007 .

[26]  Frank H. Bauer,et al.  PARTIALLY DECENTRALIZED CONTROL ARCHITECTURES FOR SATELLITE FORMATIONS , 2002 .

[27]  Dongbin Xiu,et al.  The Wiener-Askey Polynomial Chaos for Stochastic Differential Equations , 2002, SIAM J. Sci. Comput..

[28]  Steven P. Hughes,et al.  Formation Design and Sensitivity Analysis for the Magnetospheric Multiscale Mission (MMS) , 2008 .

[29]  R. Ghanem,et al.  Multi-resolution analysis of wiener-type uncertainty propagation schemes , 2004 .

[30]  Rudolph van der Merwe,et al.  The square-root unscented Kalman filter for state and parameter-estimation , 2001, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221).

[31]  R. Park,et al.  Nonlinear Mapping of Gaussian Statistics: Theory and Applications to Spacecraft Trajectory Design , 2006 .

[32]  D. Xiu Numerical Methods for Stochastic Computations: A Spectral Method Approach , 2010 .

[33]  Bin Jia,et al.  Stochastic Collocation Method for Uncertainty Propagation , 2012 .

[34]  Michèle Lavagna,et al.  Nonlinear Mapping of Uncertainties in Celestial Mechanics , 2013 .

[35]  B. Numerov,et al.  Note on the numerical integration of d2x/dt2 = f(x, t) , 1927 .

[36]  Salvatore Alfanol,et al.  A Numerical Implementation of Spherical Object Collision Probability , 2005 .

[37]  G. Karniadakis,et al.  An adaptive multi-element generalized polynomial chaos method for stochastic differential equations , 2005 .

[38]  James Agi Woodburn A Description of Filters for Minimizing the Time Required for Orbital Conjunction Computations , 2009 .

[39]  John Red-Horse,et al.  Propagation of probabilistic uncertainty in complex physical systems using a stochastic finite element approach , 1999 .

[40]  Salvatore Alfano Addressing Nonlinear Relative Motion For Spacecraft Collision Probability , 2006 .

[41]  Chris Sabol,et al.  Linearized Orbit Covariance Generation and Propagation Analysis via Simple Monte Carlo Simulations (Preprint) , 2010 .

[42]  S. A. Curtis,et al.  Magnetospheric Multiscale Mission , 2005 .

[43]  A. Doostan,et al.  Nonlinear Propagation of Orbit Uncertainty Using Non-Intrusive Polynomial Chaos , 2013 .

[44]  Gianluca Iaccarino,et al.  A least-squares approximation of partial differential equations with high-dimensional random inputs , 2009, J. Comput. Phys..

[45]  Houman Owhadi,et al.  A non-adapted sparse approximation of PDEs with stochastic inputs , 2010, J. Comput. Phys..

[46]  Jing Li,et al.  Evaluation of failure probability via surrogate models , 2010, J. Comput. Phys..

[47]  Deok-Jin Lee,et al.  Probability of Collision Error Analysis , 1999 .

[48]  James Woodburn,et al.  Determination of Close Approaches for Constellations of Satellites , 1998 .

[49]  Puneet Singla,et al.  A Hierarchical Tree Code Based Approach for Ecient Conjunction Analysis , 2012 .

[50]  Raúl Tempone,et al.  Galerkin Finite Element Approximations of Stochastic Elliptic Partial Differential Equations , 2004, SIAM J. Numer. Anal..

[51]  G. Iaccarino,et al.  Non-intrusive low-rank separated approximation of high-dimensional stochastic models , 2012, 1210.1532.

[52]  Salvatore Alfano,et al.  Determining satellite close approaches , 1992 .

[53]  F. Landis Markley,et al.  Sequential Probability Ratio Test for Collision Avoidance Maneuver Decisions , 2011 .

[54]  Ken Chan Short-Term vs. Long-Term Spacecraft Encounters , 2004 .

[55]  Aubrey B. Poore,et al.  Gaussian Sum Filters for Space Surveillance: Theory and Simulations , 2011 .

[56]  N. Wiener The Homogeneous Chaos , 1938 .

[57]  Brandon A. Jones,et al.  Comparisons of the Cubed-Sphere Gravity Model with the Spherical Harmonics , 2010 .