ristic branko (11 Ergebnisse)

Zustand: New. In.

Zustand: New. In English.

Hardcover. Zustand: Brand New. 318 pages. 9.25x6.25x0.75 inches. In Stock.

Zustand: Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | 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.

Taschenbuch. Zustand: Neu. Particle Filters for Random Set Models | Branko Ristic | Taschenbuch | xiv | Englisch | 2015 | Springer New York | EAN 9781489988843 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Zustand: New. InhaltsverzeichnisPart I Theoretical concepts: introduction suboptimal nonlinear filters a tutorial on particle filters Cramer-Rao bounds for nonlinear filtering. Part II Tracking applications: tracking a ballistic object bearings-on.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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.

Hardcover. Zustand: Brand New. 2013 edition. 188 pages. 9.00x6.25x0.50 inches. In Stock.

Zustand: Sehr gut. Zustand: Sehr gut | Seiten: 299 | Sprache: Englisch | Produktart: Bücher | Filters are the basic building block of radar defense systems that track targets, provide surveillance, avoid collisions, and guide flight. The most common type of filter is the Kalman filter. For most applications the Kalman filter is reliable and efficient, but it does have limitations. This book looks at cutting-edge particle filters that can track under conditions where Kalman filters fail. It introduces the latest advances in particle filter theory, discusses their relevance to radar tracking, and examines the defense applications of particle filters. With this hands-on guide, radar engineers can develop more accurate and reliable filter designs and more precisely predict these designs' performance.

Buch. Zustand: Neu. Neuware - Filters are the basic building block of radar defense systems that track targets, provide surveillance, avoid collisions, and guide flight. The most common type of filter is the Kalman filter. For most applications the Kalman filter is reliable and efficient, but it does have limitations. This book looks at cutting-edge particle filters that can track under conditions where Kalman filters fail. It introduces the latest advances in particle filter theory, discusses their relevance to radar tracking, and examines the defense applications of particle filters. With this hands-on guide, radar engineers can develop more accurate and reliable filter designs and more precisely predict these designs' performance.