Parallel Concatenation of Bayesian Filters: Turbo Filtering

2018/06/12 by Giorgio M. Vitetta, Pasquale Di Viesti, Vitetta, Giorgio M. +5 · 1 citation
Computer Science · Engineering · #Computation (stat.CO) #Distributed Sensor Networks and Detection Algorithms #FOS: Computer and information sciences #Target Tracking and Data Fusion in Sensor Networks #Water Systems and Optimization

paper · pdf · doi:10.48550/arxiv.1806.04632

openalex publication_date 2018/06/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

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