2009/08/31 by Boris N. Oreshkin, Mark Coates, Mark J. Coates · 7 citations
Computer Science · Engineering · Mathematics · #Algorithm #Applied mathematics #Approximation algorithm #Approximation error #Approximation theory #Computer science #Distributed Sensor Networks and Detection Algorithms #Error analysis #Exponential function #Filter (signal processing) #Mathematical analysis #Mathematical optimization #Mathematics #Node (physics) #Parametric statistics #Particle (ecology) #Particle filter #Particle system #Physics #Quantum mechanics #Statistics #Target Tracking and Data Fusion in Sensor Networks #Water Systems and Optimization #math.PR #math.ST #stat.TH
paper · pdf · doi:10.1214/11-aap760
published in The Annals of Applied Probability 21(6) (Institute of Mathematical Statistics) · Published in at http://dx.doi.org/10.1214/11-AAP760 the Annals of Applied Probability (http://www.imstat.org/aap/) by the Institute of Mathematical Statistics (http://www.imstat.org)
openalex publication_date 2011/11/23 · arxiv created 2012/02/24 · arxiv updated 2012/02/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
This paper examines the impact of approximation steps that become necessary when particle filters are implemented on resource-constrained platforms. We consider particle filters that perform intermittent approximation, either by subsampling the particles or by generating a parametric approximation. For such algorithms, we derive time-uniform bounds on the weak-sense Lp error and present associated exponential inequalities. We motivate the theoretical analysis by considering the leader node particle filter and present numerical experiments exploring its performance and the relationship to the error bounds.