Proposal distribution for particle filtering applied to terrain navigation

Abstract

This article provides a methodology for designing a proposal distribution in the context of particle filtering for terrain navigation. The suggested method is based on the use of an importance distribution centered around an estimate of the maximum a posteriori (MAP). By assuming a Gaussian prior, we show that the computation of the MAP can be reduced to an optimization problem in a space of lower state dimension. Furthermore, we introduce a new method for choosing the covariance of the proposal. In this case, numerical experiments show that the method can improve upon classical sampling methods.