TY - GEN

T1 - Information theoretic space object data association methods using an adaptive Gaussian sum filter

AU - Linares, Richard

AU - Kumar, Vishwajeet

AU - Singla, Puneet

AU - Crassidis, John L.

N1 - Copyright:
Copyright 2011 Elsevier B.V., All rights reserved.

PY - 2011

Y1 - 2011

N2 - This paper shows an approach to improve the statistical validity of orbital estimates and uncertainties as well as a method of associating measurements with the correct space objects. The approach involves using an adaptive Gaussian mixture solution to the Fokker-Planck-Kolmogorov equation for its applicability to the space object tracking problem. The Fokker-Planck-Kolmogorov equation describes the timeevolution of the probability density function for nonlinear stochastic systems with Gaussian inputs, which often results in non-Gaussian outputs. The adaptive Gaussian sum filter provides a computationally efficient and accurate solution for this equation, which captures the non-Gaussian behavior associated with these nonlinear stochastic systems. This adaptive filter is designed to be scalable, relatively efficient for solutions of this type, and thus is able to handle the nonlinear effects which are common in the estimation of resident space object orbital states. The main purpose of this paper is to develop a technique for data association based on information theoretic approaches that are compatible with the adaptive Gaussian sum filter. The adaptive filter and corresponding measurement association methods are evaluated using simulated data in realistic scenarios to determine their performance and feasibility.

AB - This paper shows an approach to improve the statistical validity of orbital estimates and uncertainties as well as a method of associating measurements with the correct space objects. The approach involves using an adaptive Gaussian mixture solution to the Fokker-Planck-Kolmogorov equation for its applicability to the space object tracking problem. The Fokker-Planck-Kolmogorov equation describes the timeevolution of the probability density function for nonlinear stochastic systems with Gaussian inputs, which often results in non-Gaussian outputs. The adaptive Gaussian sum filter provides a computationally efficient and accurate solution for this equation, which captures the non-Gaussian behavior associated with these nonlinear stochastic systems. This adaptive filter is designed to be scalable, relatively efficient for solutions of this type, and thus is able to handle the nonlinear effects which are common in the estimation of resident space object orbital states. The main purpose of this paper is to develop a technique for data association based on information theoretic approaches that are compatible with the adaptive Gaussian sum filter. The adaptive filter and corresponding measurement association methods are evaluated using simulated data in realistic scenarios to determine their performance and feasibility.

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M3 - Conference contribution

AN - SCOPUS:80053392331

SN - 9780877035695

T3 - Advances in the Astronautical Sciences

SP - 665

EP - 680

BT - Spaceflight Mechanics 2011 - Advances in the Astronautical Sciences

T2 - 21st AAS/AIAA Space Flight Mechanics Meeting

Y2 - 13 February 2011 through 17 February 2011

ER -