Synopses & Reviews
"Particle Filters for Information Fusion Using Random Sets" 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
Synopsis
This bookdiscusses 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."
Synopsis
This book covers state estimation of stochastic dynamic systems from noisy measurements, specifically sequential Bayesian estimation and nonlinear or stochastic filtering. Describes applications in multi-target systems, video tracking of pedestrians and more.
Synopsis
This book
Synopsis
"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
Synopsis
This book
Synopsis
This book
Synopsis
This book
Synopsis
This book
Synopsis
This book
Synopsis
This book
Synopsis
This book
Synopsis
This book
About the Author
Branko Ristic is at the Defence Science and Technology Organisation, Australia Defence Science and Technology Organisation, Australia
Table of Contents
3.3.2 Classification results References 4 Multi-object particle filters 4.1 Bernoulli particle filters 4.1.1 Standard Bernoulli particle filters 4.1.2 Bernoulli box-particle filter 4.2 PHD/CPDH particle filters with adaptive birth intensity 4.2.1 Extension of the PHD filter 4.2.2 Extension of the CPHD filter 4.2.3 Implementation 4.2.4 A numerical study 4.2.5 State estimation from PHD/CPHD particle filters 4.3 Particle filter approximation of the exact multi-object filter References 5 Sensor control for random set based particle filters 5.1 Bernoulli particle filter with sensor control 5.1.1 The reward function 5.1.2 Bearings only tracking in clutter with observer control 5.1.3 Target Tracking via Multi-Static Doppler Shifts 5.2 Sensor control for PHD/CPHD particle filters 5.2.1 The reward function 5.2.2 A numerical study 5.3 Sensor control for the multi-target state particle filter 5.3.1 Particle approximation of the reward function 5.3.2 A numerical study References 6 Multi-target tracking 6.1 OSPA-T: A performance metric for multi-target tracking 6.1.1 The problem and its conceptual solution 6.1.2 The base distance and labeling of estimated tracks 6.1.3 Numerical examples 6.2 Trackers based on random set filters 6.2.1 Multi-target trackers based on the Bernoulli PF 6.2.2 Multi-target trackers based on the PHD particle filter 6.2.3 Error performance comparison using the OSPA-T error 6.3 Application: Pedestrian tracking 6.3.1 Video dataset and detections 6.3.2 Description of Algorithms 6.3.3 Numerical results References 7 Advanced topics 7.1 Bernoulli filter for extended target tracking 7.1.1 Mathematical models 7.1.2 Equations of the Bernoulli filter for an extended target 7.1.3 Numerical Implementation 7.1.4 Simulation results 7.1.5 Application to a surveillance video 7.2 Calibration of tracking systems 7.2.1 Background and problem formulation 7.2.2 The proposed calibration algorithm 7.2.3 Importance sampling with progressive correction 7.2.4 Application to sensor bias estimation References Index