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Particle Filters for Random Set Models

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Specificaties

Objectstaat
Nieuw: Een nieuw, ongelezen en ongebruikt boek in perfecte staat waarin geen bladzijden ontbreken of ...
PublishedOn
2015-05-22
Title
Particle Filters for Random Set Models
ISBN
9781489988843
Publication Year
2015
Format
Trade Paperback
Language
English
Book Title
Particle Filters for Random Set Models
Author
Branko Ristic
Item Length
9.3in
Publisher
Springer New York
Genre
Technology & Engineering, Computers, Mathematics
Topic
Signals & Signal Processing, Computer Science, Applied
Item Width
6.1in
Item Weight
103.5 Oz
Number of Pages
Xiv, 174 Pages

Over dit product

Product Information

This book discusses state estimation of stochastic dynamic systems from noisy measurements, specifically sequential Bayesian estimation and nonlinear or stochastic filtering. The class of solutions presented in this book is based on the Monte Carlo statistical method. Although the resulting algorithms, known as particle filters, have been around for more than a decade, the recent theoretical developments of sequential Bayesian estimation in the framework of random set theory have provided new opportunities which are not widely known and are covered in this book. This book is ideal for graduate students, researchers, scientists and engineers interested in Bayesian estimation.

Product Identifiers

Publisher
Springer New York
ISBN-10
148998884x
ISBN-13
9781489988843
eBay Product ID (ePID)
15038401206

Product Key Features

Book Title
Particle Filters for Random Set Models
Author
Branko Ristic
Format
Trade Paperback
Language
English
Topic
Signals & Signal Processing, Computer Science, Applied
Publication Year
2015
Genre
Technology & Engineering, Computers, Mathematics
Number of Pages
Xiv, 174 Pages

Dimensions

Item Length
9.3in
Item Width
6.1in
Item Weight
103.5 Oz

Additional Product Features

Number of Volumes
1 Vol.
Lc Classification Number
Tk5102.9
Reviews
From the book reviews: "The book realizes a happy union between theory and practice. Of high interest are the Algorithms for which their pseudo-codes are presented. We think we are faced with an excellent book that will have a great success and audience between those interested for new approaches in filtering theory." (Dumitru Stanomir, zbMATH 1306.93002, 2015)
Table of Content
Introduction.- References.- Background.- A brief review of particle filters.- Online sensor control.- Non-standard measurements.- Imprecise measurements.- Imprecise measurement function.- Uncertain implication rules.- Particle filter implementation.- Applications.- Multiple objects and imperfect detection.- Random finite sets.- Multi-object stochastic filtering.- OSPA metric.- Specialized multi-object filters.- Bernoulli filter.- PHD and CPHD filter.- References.- Applications involving non-standard measurements.- Estimation using imprecise measurement models.- Localization using the received signal strength.- Prediction of an epidemic using syndromic data.- Summary.- Fusion of spatially referring natural language statements.- Language, space and modelling.- An illustrative example.- Classification using imprecise likelihoods.- Modelling.- Classification results.- References.- object particle filters.- Bernoulli particle filters.- Standard Bernoulli particle filters.- Bernoulli box-particle filter.- PHD/CPDH particle filters with adaptive birth intensity.- Extension of the PHD filter.- Extension of the CPHD filter.- Implementation.- A numerical study.- State estimation from PHD/CPHD particle filters.- Particle filter approximation of the exact multi-object filter.- References.- Sensor control for random set based particle filters.- Bernoulli particle filter with sensor control.- The reward function.- Bearings only tracking in clutter with observer control.- Target Tracking via Multi-Static Doppler Shifts.- Sensor control for PHD/CPHD particle filters.- The reward function.- A numerical study.- Sensor control for the multi-target state particle filter.- Particle approximation of the reward function.- A numerical study.- References.- Multi-target tracking.- OSPA-T: A performance metric for multi-target tracking.- The problem and its conceptual solution.- The base distance and labeling of estimated tracks.- Numerical examples.- Trackers based on random set filters.- Multi-target trackers based on the Bernoulli PF.- Multi-target trackers based on the PHD particle filter.- Error performance comparison using the OSPA-T error.- Application: Pedestrian tracking.- Video dataset and detections.- Description of Algorithms.- Numerical results.- References.- Advanced topics.- Filter for extended target tracking.- Mathematical models.- Equations of the Bernoulli filter for an extended target.- Numerical Implementation.- Simulation results.- Application to a surveillance video.- Calibration of tracking systems.- Background and problem formulation.- The proposed calibration algorithm.- Importance sampling with progressive correction.- Application to sensor bias estimation.- References.- Index.
Copyright Date
2013
Illustrated
Yes

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