Parallel recursive Bayesian estimation on multicore computational platforms using orthogonal basis functions

Abstract

A method solving the recursive Bayesian estimation problem by means of orthogonal series representations of the involved probability density functions is proposed. The coefficients of the expansion for the posterior density are recursively propagated in time via prediction and update equations. The method has two main benefits: it provides high estimation accuracy at a relatively low computational cost and is highly amenable to parallel implementation. The parallelization properties of the method are analyzed and evaluated on a shared memory multicore processor. Up to 8 cores are employed in the numerical experiments and linear speedup is achieved. An application to a bearings-only tracking problem demonstrates the low computational cost of the method by providing the same accuracy as the particle filter but with significantly less computations.