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Particle Filters for Random Set Models (Paperback, 2013 ed.): Branko Ristic Particle Filters for Random Set Models (Paperback, 2013 ed.)
Branko Ristic
R4,756 Discovery Miles 47 560 Ships in 10 - 15 working days

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.

Particle Filters for Random Set Models (Hardcover, 2013 ed.): Branko Ristic Particle Filters for Random Set Models (Hardcover, 2013 ed.)
Branko Ristic
R5,001 Discovery Miles 50 010 Ships in 10 - 15 working days

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.

Beyond The Kalman Filter - Particle Filters For Tracking Applications (Hardcover): Branko Ristic, Sanjeev Arulampalam, Neil... Beyond The Kalman Filter - Particle Filters For Tracking Applications (Hardcover)
Branko Ristic, Sanjeev Arulampalam, Neil Gordon
R4,000 Discovery Miles 40 000 Ships in 10 - 15 working days

For most tracking applications the Kalman filter is reliable and efficient, but it is limited to a relatively restricted class of linear Gaussian problems. To solve problems beyond this restricted class, particle filters are proving to be dependable methods for stochastic dynamic estimation. This cutting-edge book introduces the latest advances in particle filter theory, discusses their relevance to defence surveillance systems, and examines defence-related applications of particle filters to nonlinear and non-Gaussian problems. nonlinear filter designs and more precisely predict the performance of these designs. You can also apply particle filters to tracking a ballistic object, detection and tracking of stealthy targets, tracking through the blind Doppler zone, bi-static radar tracking, passive ranging (bearings-only tracking) of manoeuvering targets, range-only tracking, terrain-aided tracking of ground vehicles, and group and extended object tracking.

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