Everything about Particle filter
Type of Monte Carlo algorithms for signal processing and statistical inference
Particle filters, also known as sequential Monte Carlo methods, are a set of Monte Carlo algorithms used to find approximate solutions for filtering problems for nonlinear state-space systems, such as signal processing and Bayesian statistical inference. The filtering problem consists of estimating the internal states in dynamical systems when partial observations are made and random perturbations are present in the sensors as well as in the dynamical system. The objective is to compute the posterior distributions of the states of a Markov process, given the noisy and partial observations. The term "particle filters" was first coined in 1996 by Pierre Del Moral about mean-field interacting particle methods used in fluid mechanics since the beginning of the 1960s. The term "Sequential Monte Carlo" was coined by Jun S. Liu and Rong Chen in 1998. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Particle filterA 5-minute orientation
- Skim a recent paper: Hybrid Particle Filter–Particle Swarm Optimization Algorithm and Application to Fuzzy Controlled Servo SystemsInfluential work since 2022
- Pick up a classic book: High-temperature, high-pressure particulate control with ceramic bag filtersThe most-reprinted book on the subject
- Read the landmark paper: A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian trackingCited 9,125 times
- Go sideways: Polynomial interpolationA closely related topic
- Go sideways: Recursive Bayesian estimationA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🏆 Landmark papers (most cited)
A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking
Improved particle filter for nonlinearproblems
Interacting multiple model particle filter
Feedback Particle Filter
Multivariable feedback particle filter
Particle Filter Recurrent Neural Networks
Gasoline Particle Filter Development
The Iterated Auxiliary Particle Filter
🆕 Influential since 2022
Hybrid Particle Filter–Particle Swarm Optimization Algorithm and Application to Fuzzy Controlled Servo Systems
An accurate state-of-charge estimation of lithium-ion batteries based on improved particle swarm optimization-adaptive square root cubature kalman filter
A survey of feedback particle filter and related controlled interacting particle systems (CIPS)
Efficient Distributed Particle Filter for Robust Range-Only SLAM
Pedestrian Indoor Localization Method Based on Integrated Particle Filter
📚 Books worth reading
- 📕High-temperature, high-pressure particulate control with ceramic bag filtersM. A Shackleton · 1978
- 📕High-temperature, high-pressure particulate control with ceramic bag filtersM. A. Shackleton · 1978
Fine particles in gaseous mediaHoward E. Hesketh · 1977- 📕Investigation of HEPA filters subjected to tornado pressure pulses · 1978
- 📕Analysis of ventilation systems subjected to tornado conditionsR. A Martin · 1979
- 📕Coal dissolution studiesT. M Gilliam · 1979
- 📕Coal dissolution studiesT. M. Gilliam · 1979
- 📕Analysis of ventilation systems subjected to tornado conditionsR. A. Martin · 1979
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
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