Optimal SIR algorithm vs. fully adapted auxiliary particle filter: A matter of conditional independence

Particle filters (PF) and auxiliary particle filters (APF) are widely used sequential Monte Carlo (SMC) techniques. In this paper we comparatively analyse the Sampling Importance Resampling (SIR) PF with optimal conditional importance distribution (CID) and the fully adapted APF (FA-APF). Both algor...

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Veröffentlicht in:2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) S. 3992 - 3995
Hauptverfasser: Desbouvries, Francois, Petetin, Yohan, Monfrini, Emmanuel
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.05.2011
Schlagworte:
Algorithm design and analysis
Approximation algorithms
Approximation methods
Atmospheric measurements
Auxiliary Particle Filtering
Conditional Independence
Filtering
Monte Carlo methods
Particle Filtering
Particle measurements
Sequential Monte Carlo
ISBN:9781457705380, 1457705389
ISSN:1520-6149
Online-Zugang:Volltext
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Zusammenfassung:Particle filters (PF) and auxiliary particle filters (APF) are widely used sequential Monte Carlo (SMC) techniques. In this paper we comparatively analyse the Sampling Importance Resampling (SIR) PF with optimal conditional importance distribution (CID) and the fully adapted APF (FA-APF). Both algorithms share the same Sampling (S), Weighting (W) and Resampling (R) steps, and only differ in the order in which these steps are performed. The order of the operations is not unsignificant: starting at time n − 1 from a common set of particles, we show that one single updated particle at time n will marginally be sampled in both algorithms from the same probability density function (pdf), but as a whole the full set of particles will be conditionally independent if created by the FA-APF algorithm, and dependent if created by the SIR algorithm, which results in support degeneracy.
ISBN:9781457705380
1457705389
ISSN:1520-6149
DOI:10.1109/ICASSP.2011.5947227