Particle Filters for Random Set Models (Record no. 54509)
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| fixed length control field | 05711nam a22005535i 4500 |
| 001 - CONTROL NUMBER | |
| control field | 978-1-4614-6316-0 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20200421111653.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 130417s2013 xxu| s |||| 0|eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9781461463160 |
| -- | 978-1-4614-6316-0 |
| 082 04 - CLASSIFICATION NUMBER | |
| Call Number | 621.382 |
| 100 1# - AUTHOR NAME | |
| Author | Ristic, Branko. |
| 245 10 - TITLE STATEMENT | |
| Title | Particle Filters for Random Set Models |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | XIV, 174 p. |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Remark 2 | 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. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | "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. |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | http://dx.doi.org/10.1007/978-1-4614-6316-0 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | eBooks |
| 264 #1 - | |
| -- | New York, NY : |
| -- | Springer New York : |
| -- | Imprint: Springer, |
| -- | 2013. |
| 336 ## - | |
| -- | text |
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| -- | rdacontent |
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| -- | computer |
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| -- | rdamedia |
| 338 ## - | |
| -- | online resource |
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| 347 ## - | |
| -- | text file |
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| -- | rda |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Engineering. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Mathematical statistics. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Artificial intelligence. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Information theory. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Computational intelligence. |
| 650 14 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Engineering. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Signal, Image and Speech Processing. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Information and Communication, Circuits. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Probability and Statistics in Computer Science. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Artificial Intelligence (incl. Robotics). |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Computational Intelligence. |
| 912 ## - | |
| -- | ZDB-2-ENG |
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