Gaussian particle filtering

By Jayesh H. Kotecha, Petar M. Djuric. Proceedings of the 11th IEEE Signal Processing Workshop on Statistical Signal Processing (Cat. No.01TH8563), 2001

877 citations recorded by the source corpus.

Subfields: Artificial Intelligence

View Paper: https://www.semanticscholar.org/paper/1d08ae3d45f60d7a3db26cb5d4d18311fa58748d

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In Artificial Intelligence

Citations in the corpus

Marginalized particle filters for mixed linear/nonlinear state-space models (2005, background): “, the auxiliary particle filter [34] and the Gaussian particle filter [26], [27].”
Time series forecasting for nonlinear and non-stationary processes: a review and comparative study (2015, background): “Kotecha and Djuric (2003) developed a Gaussian PF model that approximated the posterior distributions by single Gaussian.”
A Numerical-Integration Perspective on Gaussian Filters (2006, methodology): “Kotecha and Djuric [8] have done a nice”
Multi-modal estimation with kernel embeddings for learning motion models (2013, background): “Gaussian particle filters also scale more favourably with dimension but are restricted to Gaussian posteriors [16].”
Resampling Methods for Particle Filtering: Classification, implementation, and strategies (2015, methodology): “More specifically, these efforts include the following: 1) Removing the resampling step from PF, such as in nonresampling PF detector [42] and Gaussian PF (GPF) [43], which are resampling-free.”
RoboADS: Anomaly Detection Against Sensor and Actuator Misbehaviors in Mobile Robots (2018, background): “Note that Gaussian distributions are standard models for noises in control system modeling [37].”
Event-Based Sensor Data Scheduling: Trade-Off Between Communication Rate and Estimation Quality (2013, neutral): “, [8], [9], [11].”
State estimation for aggressive flight in GPS-denied environments using onboard sensing (2012, methodology): “We use the Gaussian Particle Filter (GPF) to perform laser-based measurement updates [13].”
Gaussian sum particle filtering (2003, methodology): “I N [1], we introduced the Gaussian particle filter (GPF), which is used for tracking filtering and predictive distributions encountered in dynamic state-space models (DSS).”
Particle filter theory and practice with positioning applications (2010, background): “The Gaussian particle filter (GPF) in [44] fits a Gaussian distribution to the particle cloud, afte r which a new set of particles is generated from this distribution.”