A new formulation of vector weights in localized particle filters
Sign Up to like & getrecommendations! Published in 2017 at "Quarterly Journal of the Royal Meteorological Society"
DOI: 10.1002/qj.3180
Abstract: Particle filters (PFs) constitute a sequential data assimilation method based on the Monte Carlo approximation of Bayesian estimation theory. Standard PFs use scalar weights derived from the likelihood of the approximate posterior probability density functions… read more here.
Keywords: localized particle; scalar weights; particle filters; particle ... See more keywords
Multiple Model Kalman and Particle Filters and Applications: A Survey
Sign Up to like & getrecommendations! Published in 2019 at "IFAC-PapersOnLine"
DOI: 10.1016/j.ifacol.2019.06.013
Abstract: Abstract Kalman Filters (KF) is a recursive estimation algorithm, a special case of Bayesian estimators under Gaussian, linear and quadratic conditions. For non-linear systems, Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) provide first… read more here.
Keywords: kalman; particle filters; filter; model ... See more keywords
Unsupervised Learning Grouping-Based Resampling for Particle Filters
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DOI: 10.1109/access.2019.2937586
Abstract: Conventional resampling for particle filters suffers from discarding much potentially useful information due to using less information of spatial distribution of sampling particles set. An unsupervised learning grouping based resampling for particles filter, which could… read more here.
Keywords: resampling particle; particles set; particle filters; unsupervised learning ... See more keywords
Cooperative Parameter Estimation on the Unit Sphere Using a Network of Diffusion Particle Filters
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DOI: 10.1109/lsp.2020.2988421
Abstract: We introduce in this paper novel Bayesian distributed estimation algorithms for tracking the hidden state of a system that evolves on a spherical manifold. In the proposed method, different nodes on a partially-connected network run… read more here.
Keywords: cooperative parameter; particle filters; diffusion; estimation ... See more keywords
MapReduce particle filtering with exact resampling and deterministic runtime
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DOI: 10.1186/s13634-017-0505-9
Abstract: Particle filtering is a numerical Bayesian technique that has great potential for solving sequential estimation problems involving non-linear and non-Gaussian models. Since the estimation accuracy achieved by particle filters improves as the number of particles… read more here.
Keywords: particle filters; filtering exact; particle; particle filtering ... See more keywords
MagPP: Combining Particle Filters and Pedestrian Dead Reckoning Algorithm with Geomagnetism for Indoor Positioning Using Smartphone
Sign Up to like & getrecommendations! Published in 2019 at "Sensors and Materials"
DOI: 10.18494/sam.2019.2460
Abstract: Geomagnetic positioning technology has proven to be worth investigating in the field of location-based services (LBSs), but the positioning of geomagnetic technology alone will generate a certain amount of error. To overcome the ambiguity of… read more here.
Keywords: error; indoor positioning; particle filters; dead reckoning ... See more keywords