Recursive estimation of three-dimensional aircraft position using terrain-aided positioning
Nordlund, Gustafsson · IEEE International Conference on Acoustics Speech and Signal Processing · 2002
As a part of aircraft navigation three-dimensional position (horizontal position and altitude) must be computed continuously.For accuracy and reliability reasons several sensors are usually integrated together, and here we are dealing with dead-reckoning integrated with terrain-aided positioning.Terrain-aided positioning suffers from severe nonlinear structure, meaning that we have to solve a nonlinear recursive Bayesian estimation problem.This is not possible to do exactly, but recursive Monte Carlo methods, also known as particle filters, provide a promising approximate solution.To reduce the computational load of the normally rather computer intensive particle filter we present algorithms which take advantage of linear structure.These algorithms are all based on a Rao-Blackwellisation technique, i.e. we marginalise the full conditional posterior density with respect to the linear part, which here is altitude.The algorithms differ in the way the linear part is estimated, but the principle is to use multiple Kalman filters.The particle filter is then used for estimating horizontal position only.Simulations show that the computational load is reduced significantly.