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Author: Martin Spengler
Publisher:
ISBN: 9783866280212
Category :
Languages : en
Pages : 145
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Book Description
Author: Martin Spengler
Publisher:
ISBN: 9783866280212
Category :
Languages : en
Pages : 145
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Book Description
Author: Jun Feng Li
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Author: Arnaud Doucet
Publisher: Springer Science & Business Media
ISBN: 1475734379
Category : Mathematics
Languages : en
Pages : 590
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Book Description
Monte Carlo methods are revolutionizing the on-line analysis of data in many fileds. They have made it possible to solve numerically many complex, non-standard problems that were previously intractable. This book presents the first comprehensive treatment of these techniques.
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 19
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Book Description
In this project, a Monte Carlo approach to tracking was developed for tracking in cluttered environments and across multiple scales. The Monte Carlo approach was compared with an active contour approach. Specifically, we developed a novel deterministic approach for 2D projective/affine snakes, which was evaluated in conditions of high clutter and with targets of varying viewpoint and scale. A base view active contour method has been developed and tested for target tracking. The base view active contour displayed an average error 10% more accurate than the correlation tracker and 14% more accurate than the centroid tracker tested with 120 synthetic videos corrupted with both Gaussian and impulse noise. Over 46 real video sequences base view active contours successfully tracked the target in an average of 80% of the frames as compared to 73% of the frames for the centroid tracker and 83% for the correlation tracker. When the real video sequences containing target occlusion were removed from consideration, the base view active contour successfully tracked in an average 87% of the frames whereas the correlation tracker's performance dropped to only 75% of the frames. Overall, base view active contours outperform the competing methods in the synthetic and real video experiments.
Author: John Stephen Mullane
Publisher: Springer Science & Business Media
ISBN: 3642213898
Category : Technology & Engineering
Languages : en
Pages : 161
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Book Description
The monograph written by John Mullane, Ba-Ngu Vo, Martin Adams and Ba-Tuong Vo is devoted to the field of autonomous robot systems, which have been receiving a great deal of attention by the research community in the latest few years. The contents are focused on the problem of representing the environment and its uncertainty in terms of feature based maps. Random Finite Sets are adopted as the fundamental tool to represent a map, and a general framework is proposed for feature management, data association and state estimation. The approaches are tested in a number of experiments on both ground based and marine based facilities.
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 7
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Book Description
Random finite sets provide a rigorous foundation for optimal Bayes multi-target filtering. The major hurdle faced in Bayes multi-target filtering is the inherent computational intractability of the method. Even the Probability Hypothesis Density (PHD) filter, which propagates only the first moment (or PHD) instead of the full multi-target posterior, still involves multiple integrals with no closed forms. In this paper, the authors highlight the relationship between the Radon-Nikodym derivative and set derivative of random finite sets that enable a Sequential Monte Carlo (SMC) implementation of the optimal multi-target filter. In addition, a generalized SMC method to implement the PHD filter also is presented. the SMC PHD filter has an attractive feature -- its computational complexity is independent of the (time-varying) number of targets.
Author: Carine Hue
Publisher:
ISBN:
Category :
Languages : en
Pages : 16
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Author: Nicolas Chopin
Publisher: Springer Nature
ISBN: 3030478459
Category : Mathematics
Languages : en
Pages : 378
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Book Description
This book provides a general introduction to Sequential Monte Carlo (SMC) methods, also known as particle filters. These methods have become a staple for the sequential analysis of data in such diverse fields as signal processing, epidemiology, machine learning, population ecology, quantitative finance, and robotics. The coverage is comprehensive, ranging from the underlying theory to computational implementation, methodology, and diverse applications in various areas of science. This is achieved by describing SMC algorithms as particular cases of a general framework, which involves concepts such as Feynman-Kac distributions, and tools such as importance sampling and resampling. This general framework is used consistently throughout the book. Extensive coverage is provided on sequential learning (filtering, smoothing) of state-space (hidden Markov) models, as this remains an important application of SMC methods. More recent applications, such as parameter estimation of these models (through e.g. particle Markov chain Monte Carlo techniques) and the simulation of challenging probability distributions (in e.g. Bayesian inference or rare-event problems), are also discussed. The book may be used either as a graduate text on Sequential Monte Carlo methods and state-space modeling, or as a general reference work on the area. Each chapter includes a set of exercises for self-study, a comprehensive bibliography, and a “Python corner,” which discusses the practical implementation of the methods covered. In addition, the book comes with an open source Python library, which implements all the algorithms described in the book, and contains all the programs that were used to perform the numerical experiments.
Author: Marcelo G. S. Bruno
Publisher: Morgan & Claypool Publishers
ISBN: 1627051201
Category : Technology & Engineering
Languages : en
Pages : 101
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Book Description
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
Author: Augustine Tze Yik Ooi
Publisher:
ISBN:
Category : Estimation theory
Languages : en
Pages : 137
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Book Description
