Numerical Fitting-based Likelihood Calculation to Speed up the Particle Filter

TL;DR

This paper introduces a numerical fitting method to efficiently approximate likelihood calculations in particle filters, significantly reducing computation time while maintaining accuracy, enabling real-time applications in complex scenarios.

Contribution

A novel numerical fitting approach using fulcrums and kernel density estimation to speed up likelihood computation in particle filters, suitable for high-dimensional problems.

Findings

Reduces likelihood computation time significantly.

Maintains estimation accuracy with proper fitting.

Validated on benchmark and real-world localization tasks.

Abstract

The likelihood calculation of a vast number of particles is the computational bottleneck for the particle filter in applications where the observation information is rich. For fast computing the likelihood of particles, a numerical fitting approach is proposed to construct the Likelihood Probability Density Function (Li-PDF) by using a comparably small number of so-called fulcrums. The likelihood of particles is thereby analytically inferred, explicitly or implicitly, based on the Li-PDF instead of directly computed by utilizing the observation, which can significantly reduce the computation and enables real time filtering. The proposed approach guarantees the estimation quality when an appropriate fitting function and properly distributed fulcrums are used. The details for construction of the fitting function and fulcrums are addressed respectively in detail. In particular, to deal with multivariate fitting, the nonparametric kernel density estimator is presented which is flexible and convenient for implicit Li-PDF implementation. Simulation comparison with a variety of existing approaches on a benchmark 1-dimensional model and multi-dimensional robot localization and visual tracking demonstrate the validity of our approach.