INTERNATIONAL STANDARD BOOK NUMBER
(Number (ISBN
9781461463160
NATIONAL BIBLIOGRAPHY NUMBER
Country Code
IR
Number
EN-57640
LANGUAGE OF THE ITEM
.Language of Text, Soundtrack etc
انگلیسی
COUNTRY OF PUBLICATION OR PRODUCTlON
Country of publication
IR
TITLE AND STATEMENT OF RESPONSIBILITY
Title Proper
Particle Filters for Random Set Model
General Material Designation
[Book]
First Statement of Responsibility
/ by Branko Ristic
.PUBLICATION, DISTRIBUTION, ETC
Place of Publication, Distribution, etc.
New York, NY
Name of Publisher, Distributor, etc.
: Springer New York :Imprint: Springer,
Date of Publication, Distribution, etc.
, 2013.
PHYSICAL DESCRIPTION
Specific Material Designation and Extent of Item
XIV, 174 p. 52 illus., 41 illus. in color., online resource.
NOTES PERTAINING TO PUBLICATION, DISTRIBUTION, ETC.
Text of Note
Electronic
CONTENTS NOTE
Text of Note
"Particle Filters for Random Set Models" presents coverage of 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. The resulting algorithms, known as particle filters, in the last decade have become one of the essential tools for stochastic filtering, with applications ranging from navigation and autonomous vehicles to bio-informatics and finance. While 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. These recent developments have dramatically widened the scope of applications, from single to multiple appearing/disappearing objects, from precise to imprecise measurements and measurement models. This book is ideal for graduate students, researchers, scientists and engineers interested in Bayesian estimation.
Text of Note
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.
TOPICAL NAME USED AS SUBJECT
Engineering
Computer science
Artificial intelligence
Mathematics
Electronic books
LIBRARY OF CONGRESS CLASSIFICATION
Class number
E-BOOK
PERSONAL NAME - PRIMARY RESPONSIBILITY
Ristic, Branko.
PERSONAL NAME - SECONDARY RESPONSIBILITY
SpringerLink (Online service)
ORIGINATING SOURCE
Country
ایران
ELECTRONIC LOCATION AND ACCESS
Host name
9781461463153.pdf
Access number
عادی
Compression information
عادی
Date and Hour of Consultation and Access
9781461463153.pdf
Electronic Format Type
متن
old catalog
e
BL
1
a
Y
