Particle Filters for Random Set Models [Hardcover]

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.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.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 trackingl£B