Nonlinear Estimation of Hypersonic State Trajectories in Bayesian Framework with Polynomial Chaos

摘要

This paper presents a nonlinear estimation algorithm to estimate state trajectories of a hypersonic vehicle withninitial condition uncertainty. Polynomial chaos theory is used to predict the evolution of state uncertainty of thennonlinear system, and a Bayesian estimation algorithm is used to estimate the posterior probability density functionnof the nonlinear random process. The nonlinear estimation algorithm is then applied to the hypersonic reentry of anspacecraft in Martian atmosphere. Its performance is compared with estimators based on an extended Kalmannfiltering and unscented Kalman filtering framework. It is observed that for the particular application, the proposednestimator outperforms extended Kalman filtering and unscented Kalman filtering, highlighting its need in thencurrent scenario.