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Awesome GitHub RepositoriesParticle Filtering
Sequential Monte Carlo methods for estimating states in non-Gaussian or non-linear environments.
Distinct from Particle Filter Localization: Generic particle filtering for any dynamic system, whereas the sibling is restricted to robotic localization.
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Awesome Particle Filtering GitHub Repositories
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- rlabbe/filterpy
rlabbe/filterpy
3,772Ver en GitHubfilterpy is a toolkit for Bayesian state estimation, Gaussian statistical analysis, and time-series noise reduction. It provides a library of linear and non-linear Kalman filters, as well as routines for non-Gaussian state estimation and signal smoothing. The project implements a variety of estimation methods, including particle filtering using Markov Chain Monte Carlo and resampling, and discrete Bayes filtering. It also includes a suite of algorithms for refining historical state estimates through backward and fixed-lag smoothing. Additional capabilities cover multivariate Gaussian analysi
Estimates states in non-Gaussian environments using Markov Chain Monte Carlo routines and resampling.
Python
