Entropy-Based Space Object Data Association Using an Adaptive Gaussian Sum Filter
Daniel Giza, Puneet Singla, John L. Crassidis, Richard Linares, Paul Cefola, Keric A. Hill · AIAA/AAS Astrodynamics Specialist Conference · 2010
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 resident space objects and classifying events in near realtime. The approach involves using an adaptive Gaussian mixture solution to the Fokker-Planck-Kolmogorov equation for its applicability to the resident space object tracking problem. The Fokker-Planck-Kolmogorov equation describes the time-evolution of the probability density function for nonlinear stochastic systems with Gaussian inputs, which often results in non-Gaussian outputs. The adaptive Gaussian sum lter provides a computationally ecient and accurate solution for this equation, which captures the non-Gaussian behavior associated with these nonlinear stochastic systems. This adaptive lter is designed to be scalable, relatively ecient for solutions of this type, and thus is able to handle the nonlinear eects 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 entropy theory that is compatible with the adaptive Gaussian sum lter. The adaptive lter and corresponding measurement association methods are evaluated using simulated data in realistic scenarios to determine their performance and feasibility.