• Corpus ID: 122725027

A Tutorial on Particle Filtering and Smoothing: Fifteen years later

@inproceedings{Doucet2008ATO,
  title={A Tutorial on Particle Filtering and Smoothing: Fifteen years later},
  author={A. Doucet and A. M. Johansen},
  year={2008}
}
Optimal estimation problems for non-linear non-Gaussian state-space models do not typically admit analytic solutions. Since their introduction in 1993, particle filtering methods have become a very popular class of algorithms to solve these estimation problems numerically in an online manner, i.e. recursively as observations become available, and are now routinely used in fields as diverse as computer vision, econometrics, robotics and navigation. The objective of this tutorial is to provide a… 

Figures from this paper

Particle filtering with observations in a manifold
  • S. Said, J. Manton
  • Mathematics
    2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
  • 2015
TLDR
It is shown that well-chosen connector maps can be used to transform successive samples from a continuous time observation process, evolving on a manifold, into a discrete sequence of random vectors, which are asymptotically independent and normally distributed, in the limit where the sampling interval goes to zero.
Particle flow for nonlinear filters, Bayesian decisions and transport
  • F. Daum, Jim Huang
  • Engineering
    Proceedings of the 16th International Conference on Information Fusion
  • 2013
We derive a new algorithm for particle flow with non-zero diffusion corresponding to Bayes' rule, and we report the results of Monte Carlo simulations which show that the new filter is an order of
AN OVERVIEW OF EFFICIENT NONLINEAR FILTERING - FROM KALMAN FILTER TO PARTICLE FILTERS TO EIS
Generally, in most applied fields, the dynamic state space models are of nonlinearity with non-Gaussian noise. However, as a famous and simple algorithmic filter, Kalman filter can only estimate
Particle filters
Filtering is engineering terminology for extracting information about a signal from partial and noisy observations. In geophysics, filtering is usually called data assimilation. In the last 50 years,
Resampling Methods for Particle Filtering: Classification, implementation, and strategies
TLDR
The state of the art of resampling methods was reviewed and the methods were classified and their properties were compared in the framework of the proposed classifications to provide guidelines to practitioners and researchers.
A Practical Example for the Non-linear Bayesian Filtering of Model Parameters
TLDR
This tutorial considers the non-linear Bayesian filtering of static parameters in a time-dependent model of gravitational acceleration by focusing on particle-based filters and presents Sequential Importance Sampling and Sequential Monte Carlo.
Particle Filters and Data Assimilation
TLDR
The challenges posed by models with high-dimensional states, joint estimation of parameters and the state, and inference for the history of the state process are discussed, including methods based on the particle filter and the ensemble Kalman filter.
...
...

References

SHOWING 1-10 OF 44 REFERENCES
A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking
TLDR
Both optimal and suboptimal Bayesian algorithms for nonlinear/non-Gaussian tracking problems, with a focus on particle filters are reviewed.
A tutorial on particle filters for on-line nonlinear/non-Gaussian Bayesian tracking
TLDR
Both optimal and suboptimal Bayesian algorithms for nonlinear/non-Gaussian tracking problems, with a focus on particle filters are reviewed.
An improved particle filter for non-linear problems
The Kalman filter provides an effective solution to the linear Gaussian filtering problem. However where there is nonlinearity, either in the model specification or the observation process, other
A survey of convergence results on particle filtering methods for practitioners
TLDR
The aim of this paper is to present a survey of convergence results on particle filtering methods to make them accessible to practitioners.
Novel approach to nonlinear/non-Gaussian Bayesian state estimation
TLDR
An algorithm, the bootstrap filter, is proposed for implementing recursive Bayesian filters, represented as a set of random samples, which are updated and propagated by the algorithm.
Particle filtering for partially observed Gaussian state space models
TLDR
This work proposes a special particle filtering method which uses random mixtures of normal distributions to represent the posterior distributions of partially observed Gaussian state space models, based on a marginalization idea for improving efficiency.
Monte Carlo Filter and Smoother for Non-Gaussian Nonlinear State Space Models
TLDR
A new algorithm based on a Monte Carlo method that can be applied to a broad class of nonlinear non-Gaussian higher dimensional state space models on the provision that the dimensions of the system noise and the observation noise are relatively low.
An Overview of Existing Methods and Recent Advances in Sequential Monte Carlo
TLDR
This paper is intended to serve both as an introduction to SMC algorithms for nonspecialists and as a reference to recent contributions in domains where the techniques are still under significant development, including smoothing, estimation of fixed parameters and use of SMC methods beyond the standard filtering contexts.
The Unscented Particle Filter
TLDR
This paper proposes a new particle filter based on sequential importance sampling that outperforms standard particle filtering and other nonlinear filtering methods very substantially and is in agreement with the theoretical convergence proof for the algorithm.
Improvement Strategies for Monte Carlo Particle Filters
TLDR
This paper presents a general importance sampling framework for the filtering/smoothing problem and shows how the standard techniques can be obtained from this general approach, and describes the role of MCMC resampling as proposed by Gilks and Berzuini and MacEachern, Clyde and Liu 1999.
...
...