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  • Produktbild: Particle Filters for Random Set Models
  • Produktbild: Particle Filters for Random Set Models

Particle Filters for Random Set Models

Fr. 182.00

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Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

15.04.2013

Verlag

Springer Us

Seitenzahl

174

Maße (L/B/H)

24.1/16/1.4 cm

Gewicht

453 g

Auflage

2013

Sprache

Englisch

ISBN

978-1-4614-6315-3

Beschreibung

Rezension

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)

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

15.04.2013

Verlag

Springer Us

Seitenzahl

174

Maße (L/B/H)

24.1/16/1.4 cm

Gewicht

453 g

Auflage

2013

Sprache

Englisch

ISBN

978-1-4614-6315-3

Herstelleradresse

Springer-Verlag GmbH
Tiergartenstr. 17
69121 Heidelberg
DE

Email: GPSR Kontakt

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  • Produktbild: Particle Filters for Random Set Models
  • Produktbild: Particle Filters for Random Set Models
  • 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.