Particle filter theory and practice with positioning applications

By Fredrik K. Gustafsson. IEEE Aerospace and Electronic Systems Magazine, 2010

750 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Environmental Engineering, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/9bd0c2f861665f3c68f405c468494add7efba428

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Citations in the corpus

Learning Switching Models for Abnormality Detection for Autonomous Driving (2018, methodology): “A Particle Filter (PF) allows one to manage a numerical approximation of the posterior in a nonlinear, non-Gaussian Bayesian filtering problem [16].”
A comprehensive review of digital twin — part 1: modeling and twinning enabling technologies (2022, methodology): “As new measurements from the physical system become available, Bayesian filters (Cox 1964; Gustafsson 2010), such as the extended Kalman filter and particle filters, can be used to solve this state-state model for online updating the ML model parameters.”
Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMC (2013, background): “We address this problem in a fundamentally different way by marginalizing out f , allowing us to respect the non-parametric nature of the model.”
Evolution of Indoor Positioning Technologies: A Survey (2017, methodology): “This process is repeated in an iterativeway [47].”
DanceTrack: Multi-Object Tracking in Uniform Appearance and Diverse Motion (2021, methodology): “Sequential analysis tools such as Particle filter [14,15] and Kalman filter [16] are found efficient in such applications, for example, SORT [3] is developed on the Kalman filter motion model.”
An efficient stochastic approximation EM algorithm using conditional particle filters (2013, methodology): “The method is most easily described as an auxiliary PF (see e.g. [1] [21] [22] for an introduction).”