Parallel Concatenation of Bayesian Filters: Turbo Filtering

TL;DR

This paper introduces turbo filtering, a novel approach that combines two Bayesian filters in parallel using a new graphical model, improving the complexity-accuracy tradeoff for certain systems.

Contribution

It presents a new graphical model and two filtering algorithms for conditionally linear Gaussian systems, enhancing efficiency over existing methods.

Findings

Better complexity-accuracy tradeoff than marginalized particle filtering

Effective for conditionally linear Gaussian systems

Numerical results demonstrate improved performance

Abstract

In this manuscript a method for developing novel filtering algorithms through the parallel concatenation of two Bayesian filters is illustrated. Our description of this method, called turbo filtering, is based on a new graphical model; this allows us to efficiently describe both the processing accomplished inside each of the constituent filter and the interactions between them. This model is exploited to develop two new filtering algorithms for conditionally linear Gaussian systems. Numerical results for a specific dynamic system evidence that such filters can achieve a better complexity-accuracy tradeoff than marginalized particle filtering.