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Semi-independent resampling for particle filtering

Abstract : 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
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Contributor : Médiathèque Télécom Sudparis & Institut Mines-Télécom Business School <>
Submitted on : Thursday, December 21, 2017 - 1:16:54 PM
Last modification on : Thursday, March 4, 2021 - 3:06:02 PM

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



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