Efficient grid-based Bayesian estimation of nonlinear low-dimensional systems with sparse non-Gaussian PDFs

TL;DR

This paper presents a computationally efficient grid-based Bayesian estimation method for nonlinear systems with non-Gaussian uncertainties, using phase space discretization and localized updates to avoid resampling.

Contribution

It introduces a novel grid-based Bayesian estimation technique that reduces computational costs by focusing on active cells and evolving the PDF via the Kolmogorov forward equation.

Findings

Reduces computational expense of grid-based Bayesian methods.

Accurately tracks non-Gaussian PDFs in nonlinear systems.

Avoids resampling issues of particle-based methods.

Abstract

Bayesian estimation strategies represent the most fundamental formulation of the state estimation problem available, and apply readily to nonlinear systems with non-Gaussian uncertainties. The present paper introduces a novel method for implementing grid-based Bayesian estimation which largely sidesteps the severe computational expense that has prevented the widespread use of such methods. The method represents the evolution of the probability density function (PDF) in phase space, $p_{\x}(\x',t)$, discretized on a fixed Cartesian grid over {\it all} of phase space, and consists of two main steps: (i) Between measurement times, $p_{\x}(\x',t)$ is evolved via numerical discretization of the Kolmogorov forward equation, using a Godunov method with second-order corner transport upwind correction and a total variation diminishing flux limiter; (ii) at measurement times, $p_{\x}(\x',t)$ is updated via Bayes' theorem. Computational economy is achieved by exploiting the localised nature of $p_{\x}(\x',t)$. An ordered list of cells with non-negligible probability, as well as their immediate neighbours, is created and updated, and the PDF evolution is tracked {\it only} on these active cells. %The grid-based discretization of $p_{\x}(\x',t)$ in this approach avoids the requirement for resampling associated with particle-based representations of the PDF.