ORBIT DETERMINATION USING NONLINEAR PARTICLE FILTER AND GPS MEASUREMENTS

A particle filter, specifically a Bayesian bootstrap filter algorithm, is applied for estimating the state vector that characterizes the orbit of a satellite, using a set of GPS measurements. The development will be evaluated through performance and computational cost, comparing the bootstrap algorithm results against the unscented Kalman filter (UKF) solution for the same problem. The orbit determination is a nonlinear problem, with respect to the dynamics and the measurements equations, in which the disturbing forces and the measurements are not easily modeled. It consists essentially of estimating values that completely specify the body trajectory in the space, processing a set of measurements related to this body. Such observations can be collected through a tracking network grounded on Earth or through sensors, like GPS receivers onboard the satellite. The GPS is a wide spread system that allows computation of orbits for artificial Earth satellites by providing many redundant measurements (pseudo-ranges). The bootstrap filter is proposed for implementing recursive Bayesian filters. It is a statistical, brute-force approach to estimation that often works well for systems that are highly nonlinear. Here, the bootstrap particle filter will be implemented with resampling and roughening, a scheme for combating the reduction in the number of truly distinct sample values.