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020 _a9783031025358
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024 7 _a10.1007/978-3-031-02535-8
_2doi
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072 7 _aTEC000000
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082 0 4 _a620
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100 1 _aG. S. Bruno, Marcelo.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_987198
245 1 0 _aSequential Monte Carlo Methods for Nonlinear Discrete-Time Filtering
_h[electronic resource] /
_cby Marcelo G. S. Bruno, Marcelo G.S.
250 _a1st ed. 2013.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2013.
300 _aXI, 87 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSynthesis Lectures on Signal Processing,
_x1932-1694
505 0 _aIntroduction -- 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 _aIn 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.
650 0 _aEngineering.
_99405
650 0 _aElectrical engineering.
_987200
650 0 _aSignal processing.
_94052
650 1 4 _aTechnology and Engineering.
_987202
650 2 4 _aElectrical and Electronic Engineering.
_987204
650 2 4 _aSignal, Speech and Image Processing.
_931566
700 1 _aG.S., Marcelo.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_987206
710 2 _aSpringerLink (Online service)
_987208
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031014079
776 0 8 _iPrinted edition:
_z9783031036637
830 0 _aSynthesis Lectures on Signal Processing,
_x1932-1694
_987210
856 4 0 _uhttps://doi.org/10.1007/978-3-031-02535-8
912 _aZDB-2-SXSC
942 _cEBK
999 _c86065
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