Semi-independent resampling for particle filtering

Roland Lamberti (1, 2, 3) , Yohan Petetin (4, 1, 2, 3) , François Desbouvries (4, 1, 2, 3) , François Septier (5, 6)

1 TSP - CITI - Communications, Images et Traitement de l'Information
2 SAMOVAR - Services répartis, Architectures, MOdélisation, Validation, Administration des Réseaux
3 CNRS - Centre National de la Recherche Scientifique
4 TIPIC-SAMOVAR - Traitement de l'Information Pour Images et Communications
5 IMT Lille Douai - Ecole nationale supérieure Mines-Télécom Lille Douai
6 CRIStAL - Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189

Among sequential Monte Carlo methods, sampling importance resampling (SIR) algorithms are based on importance sampling and on some (resampling-based) rejuvenation algorithm that aims at fighting against weight degeneracy. However, this mechanism tends to be insufficient when applied to informative or high-dimensional models. In this letter, we revisit the rejuvenation mechanism and propose a class of parameterized SIR-based solutions that enable us to adjust the tradeoff between computational cost and statistical performances

Mots clés: Resampling; Sequential Monte Carlo algorithms; Sequential Monte Carlo methods; Particle filtering

Domaines: Traitement du signal et de l'image [eess.SP]

Roland Lamberti, Yohan Petetin, François Desbouvries, François Septier. Semi-independent resampling for particle filtering. IEEE Signal Processing Letters, 2018, 25 (1), pp.130 - 134. ⟨10.1109/LSP.2017.2775150⟩. ⟨hal-01670395⟩