Bayesian Inference in Terrain Navigation

Terrain navigation is a concept for autonomous aircraft navigation. If measurements of the terrain height over mean sea-level are collected along the aircraft flight path, an estimate of the aircraft position can be formed by matching these measurements with a digital reference terrain map. This matching is a recursive nonlinear estimation problem. Due to the unstructured nonlinear reference map, local approximation schemes, like the extended Kalman Filter, fail in this application. In this work, the optimal Bayesian approach to the recursive inference of the measurement sources is taken. In the Bayesian approach the uncertainty about the aircraft position is condensed in the conditional probability density function. The analytical expression for the recursive propagation of this function is derived. Due to the unstructured nonlinear terrain reference map, the propagation of the conditional density is impossible to perform in practice. To circumvent this problem, an approx..