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Resampling Methods for Particle Filtering: Classification, implementation, and strategies

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PROJECT TITLE :

Resampling Methods for Particle Filtering: Classification, implementation, and strategies

ABSTRACT:

2 decades ago, with the publication, we witnessed the rebirth of particle filtering (PF) as a methodology for sequential signal processing. Since then, PF has become terribly standard because of its ability to process observations represented by nonlinear state-space models where the noises of the model will be non-Gaussian. This methodology has been adopted in various fields, together with finance, geophysical systems, wireless communications, management, navigation and tracking, and robotics. The popularity of PF has conjointly spurred the publication of many review articles. In this text, the state-of-the-art of resampling ways was reviewed. The methods were classified and their properties were compared in the framework of the proposed classifications. The emphasis within the article was on the classification and qualitative descriptions of the algorithms. The intention was to supply guidelines to practitioners and researchers.


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Resampling Methods for Particle Filtering: Classification, implementation, and strategies - 4.8 out of 5 based on 25 votes

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