Sequential Monte Carlo Methods for Nonlinear Discrete-Time Filtering (Record no. 86065)
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| fixed length control field | 04740nam a22005175i 4500 |
| 001 - CONTROL NUMBER | |
| control field | 978-3-031-02535-8 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20240730165034.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 220601s2013 sz | s |||| 0|eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9783031025358 |
| -- | 978-3-031-02535-8 |
| 082 04 - CLASSIFICATION NUMBER | |
| Call Number | 620 |
| 100 1# - AUTHOR NAME | |
| Author | G. S. Bruno, Marcelo. |
| 245 10 - TITLE STATEMENT | |
| Title | Sequential Monte Carlo Methods for Nonlinear Discrete-Time Filtering |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 1st ed. 2013. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | XI, 87 p. |
| 490 1# - SERIES STATEMENT | |
| Series statement | Synthesis Lectures on Signal Processing, |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Remark 2 | Introduction -- Bayesian Estimation of Static Vectors -- The Stochastic Filtering Problem -- Sequential Monte Carlo Methods -- Sampling/Importance Resampling (SIR) Filter -- Importance Function Selection -- Markov Chain Monte Carlo Move Step -- Rao-Blackwellized Particle Filters -- Auxiliary Particle Filter -- Regularized Particle Filters -- Cooperative Filtering with Multiple Observers -- Application Examples -- Summary. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | In these notes, we introduce particle filtering as a recursive importance sampling method that approximates the minimum-mean-square-error (MMSE) estimate of a sequence of hidden state vectors in scenarios where the joint probability distribution of the states and the observations is non-Gaussian and, therefore, closed-form analytical expressions for the MMSE estimate are generally unavailable. We begin the notes with a review of Bayesian approaches to static (i.e., time-invariant) parameter estimation. In the sequel, we describe the solution to the problem of sequential state estimation in linear, Gaussian dynamic models, which corresponds to the well-known Kalman (or Kalman-Bucy) filter. Finally, we move to the general nonlinear, non-Gaussian stochastic filtering problem and present particle filtering as a sequential Monte Carlo approach to solve that problem in a statistically optimal way. We review several techniques to improve the performance of particle filters, including importance function optimization, particle resampling, Markov Chain Monte Carlo move steps, auxiliary particle filtering, and regularized particle filtering. We also discuss Rao-Blackwellized particle filtering as a technique that is particularly well-suited for many relevant applications such as fault detection and inertial navigation. Finally, we conclude the notes with a discussion on the emerging topic of distributed particle filtering using multiple processors located at remote nodes in a sensor network. Throughout the notes, we often assume a more general framework than in most introductory textbooks by allowing either the observation model or the hidden state dynamic model to include unknown parameters. In a fully Bayesian fashion, we treat those unknown parameters also as random variables. Using suitable dynamic conjugate priors, that approach can be applied then to perform joint state and parameter estimation. Table of Contents: Introduction / Bayesian Estimation of Static Vectors / The Stochastic Filtering Problem / Sequential Monte Carlo Methods / Sampling/Importance Resampling (SIR) Filter / Importance Function Selection / Markov Chain Monte Carlo Move Step / Rao-Blackwellized Particle Filters / Auxiliary Particle Filter / Regularized Particle Filters / Cooperative Filtering with Multiple Observers / Application Examples / Summary. |
| 700 1# - AUTHOR 2 | |
| Author 2 | G.S., Marcelo. |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | https://doi.org/10.1007/978-3-031-02535-8 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | eBooks |
| 264 #1 - | |
| -- | Cham : |
| -- | Springer International Publishing : |
| -- | Imprint: Springer, |
| -- | 2013. |
| 336 ## - | |
| -- | text |
| -- | txt |
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| -- | computer |
| -- | c |
| -- | rdamedia |
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| -- | online resource |
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| -- | text file |
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| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Engineering. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Electrical engineering. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Signal processing. |
| 650 14 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Technology and Engineering. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Electrical and Electronic Engineering. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Signal, Speech and Image Processing. |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE | |
| -- | 1932-1694 |
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| -- | ZDB-2-SXSC |
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