A Non-Gaussian Bayesian Filter Using Power and Generalized Logarithmic Moments

TL;DR

This paper introduces a novel non-Gaussian Bayesian filter that employs power and generalized logarithmic moments to accurately estimate system states, reducing the curse of dimensionality and applicable to complex, continuous functions.

Contribution

The paper proposes a new density surrogate based on combined moments, with a proven unique mapping, enabling effective non-Gaussian filtering for continuous state systems.

Findings

The proposed filter outperforms traditional methods in complex function estimation.

Simulation results demonstrate improved accuracy in mixture models.

Application to robot localization validates practical effectiveness.

Abstract

In this paper, we aim to propose a consistent non-Gaussian Bayesian filter of which the system state is a continuous function. The distributions of the true system states, and those of the system and observation noises, are only assumed Lebesgue integrable with no prior constraints on what function classes they fall within. This type of filter has significant merits in both theory and practice, which is able to ameliorate the curse of dimensionality for the particle filter, a popular non-Gaussian Bayesian filter of which the system state is parameterized by discrete particles and the corresponding weights. We first propose a new type of statistics, called the generalized logarithmic moments. Together with the power moments, they are used to form a density surrogate, parameterized as an analytic function, to approximate the true system state. The map from the parameters of the proposed density surrogate to both the power moments and the generalized logarithmic moments is proved to be a diffeomorphism, establishing the fact that there exists a unique density surrogate which satisfies both moment conditions. This diffeomorphism also allows us to use gradient methods to treat the convex optimization problem in determining the parameters. Last but not least, simulation results reveal the advantage of using both sets of moments for estimating mixtures of complicated types of functions. A robot localization simulation is also given, as an engineering application to validate the proposed filtering scheme.