Particle filtering

By Petar M. Djuric, Jayesh H. Kotecha, Jian-Qi Zhang, Yufei Huang, T. Ghirmai, Mónica F. Bugallo, J. Míguez. IEEE Signal Processing Magazine, 2003

547 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/3c2b6b6bd1f4a4ad6a1fd6864524a6b282219103

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Citations in the corpus

An Overview of Existing Methods and Recent Advances in Sequential Monte Carlo (2007, background): “This tutorial aims to highlight basic methodology and up and coming areas for particle filtering; more established topics and a range of applications are extensively reviewed in a number of excellent papers [22], [23], [24], [25], [26], [27].”
The Factor Graph Approach to Model-Based Signal Processing (2007, background): “This message type allows to describe particle filters [52], [53] as message passing algorithms (see, e.”
Distributed compression-estimation using wireless sensor networks (2006, background): “, [15] and [16]) for which an extended SOIKF to nonlinear models appears to be particularly attractive.”
Rethinking the Effective Sample Size (2018, methodology): “Nevertheless, ÊSS is used mostly in algorithms with multiple proposals, as in adaptive importance sampling (AIS) or sequential Monte Carlo methods (SMC) methods [9, 12, 7, 5, 11, 16, 20, 35, 13].”
Uncertainty assessment of hydrologic model states and parameters: Sequential data assimilation using the particle filter (2005, methodology): “SMC methods, also known as particle filters, have garnered considerable attention among researchers in communication theory, signal processing, and target tracking [Djurić et al., 2003].”
Resampling algorithms and architectures for distributed particle filters (2005, background): “They show great promise in addressing a wide variety of complex problems [6], [18].”