Journal ArticleDOI
Monte Carlo techniques for prediction and filtering of non-linear stochastic processes
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TLDR
In this article, the problem of estimating the conditional mean of the posterior density function is formulated as a multidimensional integral and the control variate method presented shows that the Monte Carlo approach can successfully be adapted to estimate the approximation error of existing nonlinear filtering equations and to improve their accuracy significantly.About:
This article is published in Automatica.The article was published on 1970-07-01. It has received 194 citations till now. The article focuses on the topics: Monte Carlo integration & Hybrid Monte Carlo.read more
Citations
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Journal ArticleDOI
On sequential Monte Carlo sampling methods for Bayesian filtering
TL;DR: An overview of methods for sequential simulation from posterior distributions for discrete time dynamic models that are typically nonlinear and non-Gaussian, and how to incorporate local linearisation methods similar to those which have previously been employed in the deterministic filtering literature are shown.
Proceedings ArticleDOI
Monte Carlo localization for mobile robots
TL;DR: The Monte Carlo localization method is introduced, where the probability density is represented by maintaining a set of samples that are randomly drawn from it, and it is shown that the resulting method is able to efficiently localize a mobile robot without knowledge of its starting location.
Book
Inference in Hidden Markov Models
TL;DR: This book is a comprehensive treatment of inference for hidden Markov models, including both algorithms and statistical theory, and builds on recent developments to present a self-contained view.
Proceedings Article
Monte Carlo localization: efficient position estimation for mobile robots
TL;DR: Monte Carlo Localization is a version of Markov localization, a family of probabilistic approaches that have recently been applied with great practical success and yields improved accuracy while requiring an order of magnitude less computation when compared to previous approaches.
Sigma-point kalman filters for probabilistic inference in dynamic state-space models
TL;DR: This work has consistently shown that there are large performance benefits to be gained by applying Sigma-Point Kalman filters to areas where EKFs have been used as the de facto standard in the past, as well as in new areas where the use of the EKF is impossible.
References
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Journal ArticleDOI
New Results in Linear Filtering and Prediction Theory
R. E. Kalman,R. S. Bucy +1 more
TL;DR: The Duality Principle relating stochastic estimation and deterministic control problems plays an important role in the proof of theoretical results and properties of the variance equation are of great interest in the theory of adaptive systems.
Journal ArticleDOI
Approximations to optimal nonlinear filters
TL;DR: In this article, the signal and noise processes are given as solutions to nonlinear stochastic differential equations and several methods of obtaining possibly useful finite dimensional approximations are considered, and some of the special problems of simulation are discussed.
Journal ArticleDOI
Monte Carlo techniques to estimate the conditional expectation in multi-stage non-linear filtering†
J. E. Handschin,David Q. Mayne +1 more
TL;DR: Using Bayes' theorem, the conditional mean of the posterior probability density function is estimated via Monte Carlo techniques as discussed by the authors, which can also be interpreted as an accuracy improvement of approximate non-linear filter equations.
Book ChapterDOI
Approximate Continuous Nonlinear Minimal-Variance Filtering
TL;DR: In this paper, the authors discuss approximate continuous nonlinear minimal-variance filtering, which is a form of sequential stochastic estimation and has its roots in the early least-squares differential correction schemes for orbit determination.