Non-Bayesian particle filters

TL;DR

This paper introduces a non-Bayesian iterative particle filtering method that reduces computational expense by directly sampling the relevant probability density functions, demonstrated through a detailed example.

Contribution

It proposes a novel non-Bayesian approach to particle filtering that bypasses the traditional Bayesian updating process, improving efficiency.

Findings

Reduces computational cost of particle filtering

Demonstrates effectiveness with a detailed example

Provides an alternative to Bayesian particle filters

Abstract

Particle filters for data assimilation in nonlinear problems use "particles" (replicas of the underlying system) to generate a sequence of probability density functions (pdfs) through a Bayesian process. This can be expensive because a significant number of particles has to be used to maintain accuracy. We offer here an alternative, in which the relevant pdfs are sampled directly by an iteration. An example is discussed in detail.