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008			230503s2013 sz | o |||| 0|eng d
020			_a9783031025358
024	7		_a10.1007/978-3-031-02535-8 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aTK5102.9 _b2013 EB
100	1		_aBruno, Marcelo G. S. _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9688327
245	1	0	_aSequential Monte Carlo Methods for Nonlinear Discrete-Time Filtering _cby Marcelo G. S. Bruno, Marcelo G. S.
250			_a1st edition 2013
264		1	_aCham _bSpringer International Publishing _c2013
300			_a1 recurso en línea (XI, 87 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_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.
988			_aSynthesis Collection of Technology_2013
650		7	_2embne _9150608 _aProceso de señales _xModelos matemáticos
650		7	_2embne _9687635 _aFiltros eléctricos digitales
650		7	_2embne _9681471 _aMétodo de Monte Carlo
776	0	8	_iPrinted edition: _z9783031014079
776	0	8	_iPrinted edition: _z9783031036637
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-02535-8 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b05/2023 _dz _eIG _zPRE