Particle filtering methods provide powerful techniques for solving non-linear state-estimation problems, and are applied to a variety of application areas in signal processing. Be...
Sankalita Saha, Neal K. Bambha, Shuvra S. Bhattach...
Abstract— Particle Filters have been widely used as a powerful optimization tool for nonlinear, non-Gaussian dynamic models such as Simultaneous Localization and Mapping (SLAM) a...
Rao-Blackwellized particle filters have become a popular tool to solve the simultaneous localization and mapping problem. This technique applies a particle filter in which each ...
Giorgio Grisetti, Gian Diego Tipaldi, Cyrill Stach...
Particle filtering is an approach to Bayesian estimation of intractable posterior distributions from time series signals distributed by non-Gaussian noise. A couple of variant par...
This paper introduces a distributed auxiliary particle filter for target tracking in sensor networks. Nodes maintain a shared particle filter by coming to a consensus about the ...