Nonlinear Estimation of Hypersonic Flight Using Polynomial Chaos
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In this paper we present a nonlinear estimation algorithm to estimate state trajectories of a hypersonic vehicle due to uncertainty in initial conditions. Polynomial chaos theory is used to predict the evolution of uncertainty of the nonlinear system, and Bayesian estimation algorithm is used to estimate the posterior non Gaussian probability density function of the random process. The state estimates are found, minimizing the variance of the posterior density function. The nonlinear estimation algorithm is then applied to the hypersonic re-entry of a spacecraft in Martian atmosphere. Its performance is compared with linear estimators based on extended Kalman filtering framework. We observe that for the particular application, the proposed estimator outperforms the linear estimator, thus highlighting the need of nonlinear estimator in this scenario. Copyright 2010 by Parikshit Dutta and Raktim Bhattacharya.
