Reduced Nonlinear Model for Orbit Uncertainty Propagation and Estimation
Javier Roa, Ryan S. Park
Abstract
This paper presents a novel method for nonlinear uncertainty propagation and estimation in orbital dynamics. The proposed technique relies on a Taylor series expansion of the integral flow to model the dynamics around the reference solution and introduces an approximation of the high-order variational equations that reduces the complexity of evaluating the series. In particular, the high-order state-transition tensors (STTs) are approximated by capturing the dominant secular terms. Simple expressions to compute them are provided. The approximation stems from confining the Lyapunov instability of the motion to the time domain. The result is a time-explicit approximation of the STTs that can be used to predict the evolution of the uncertainty distribution accounting for nonlinear effects with minimal overhead. Finally, a high-order version of the extended Kalman filter is developed by implementing the approximation of the nonlinear terms of the Taylor series into an estimation scheme. The performance of the algorithm is evaluated with several practical examples.