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

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