Presents a hands-on engineering approach to filtering algorithms and their implementation
Covers a new generation of particle filters, which are applicable to a much wider class of signal processing applications
Includes sensor control for particle filters
Provides information on a number of interesting and relevant applications, which illustrate theoretical concepts and demonstrate the performance of developed particle filters
Presents a hands-on engineering approach to filtering algorithms and their implementation
Covers a new generation of particle filters, which are applicable to a much wider class of signal processing applications
Includes sensor control for particle filters
Provides information on a number of interesting and relevant applications, which illustrate theoretical concepts and demonstrate the performance of developed particle filters
Branko Ristic is at the Defence Science and Technology Organisation, Australia
Defence Science and Technology Organisation, Australia
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.
3.3.2 Classification resultsReferences4 Multi-object particle filters4.1 Bernoulli particle filters4.1.1 Standard Bernoulli particle filters4.1.2 Bernoulli box-particle filter4.2 PHD/CPDH particle filters with adaptive birth intensity4.2.1 Extension of the PHD filter4.2.2 Extension of the CPHD filter4.2.3 Implementation
4.2.4 A numerical study4.2.5 State estimation from PHD/CPHD particle filters4.3 Particle filter approximation of the exact multi-object filterReferences5 Sensor control for random set based particle filters5.1 Bernoulli particle filter with sensor control5.1.1 The reward function5.1.2 Bearings only tracking in clutter with observer control5.1.3 Target Tracking via Multi-Static Doppler Shifts5.2 Sensor control for PHD/CPHD particle filters5.2.1 The reward function5.2.2 A numerical study5.3 Sensor control for the multi-target state particle filter5.3.1 Particle approximation of the reward function5.3.2 A numerical studyReferences6 Multi-target tracking6.1 OSPA-T: A performance metric for multi-target tracking6.1.1 The problem and its conceptual solution6.1.2 The base distance and labeling of estimated tracks6.1.3 Numerical examples6.2 Trackers based on random set filters6.2.1 Multi-target trackers based on the Bernoulli PF6.2.2 Multi-target trackers based on the PHD particle filter6.2.3 Error performance comparison using the OSPA-T error6.3 Application: Pedestrian tracking6.3.1 Video dataset and detections6.3.2 Deillegalscription of Algorithms6.3.3 Numerical resultsReferences7 Advanced topics7.1 Bernoulli filter for extended target tracking7.1.1 Mathematical models7.1.2 Equations of the Bernoulli filter for an extended target7.1.3 Numerical Implementation7.1.4 Simulation results7.1.5 Application to a surveillance video7.2 Calibration of tracking systems7.2.1 Background and problem formulation7.2.2 The proposed calibration algorithm7.2.3 Importance sampling with progressive correction7.2.4 Application to sensor bias estimationReferencesIndex
"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.
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)
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.
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.- Deillegalscription 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.
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)
Über den Autor
Branko Ristic is at the Defence Science and Technology Organisation, Australia
Defence Science and Technology Organisation, Australia
Inhaltsverzeichnis
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.- Deillegalscription 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.
Klappentext
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.
Presents a hands-on engineering approach to filtering algorithms and their implementation
Covers a new generation of particle filters, which are applicable to a much wider class of signal processing applications
Includes sensor control for particle filters
Provides information on a number of interesting and relevant applications, which illustrate theoretical concepts and demonstrate the performance of developed particle filters
