wyszukanych pozycji: 3
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Particle Filters for Random Set Models
ISBN: 9781461463153 / Angielski / Twarda / 2013 / 174 str. Termin realizacji zamówienia: ok. 20 dni roboczych. 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...
This book discusses state estimation of stochastic dynamic systems from noisy measurements, specifically sequential Bayesian estimation and nonlinear ...
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cena:
540,99 zł |
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Particle Filters for Random Set Models
ISBN: 9781489988843 / Angielski / Miękka / 2015 / 174 str. Termin realizacji zamówienia: ok. 20 dni roboczych. 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...
This bookdiscusses state estimation of stochastic dynamic systems from noisy measurements, specifically sequential Bayesian estimation and nonlinear o...
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cena:
540,99 zł |
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Beyond the Kalman Filter: Particle Filters for Tracking Applications
ISBN: 9781580536318 / Angielski / Twarda / 2004 / 318 str. Termin realizacji zamówienia: ok. 30 dni roboczych. 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...
For most tracking applications the Kalman filter is reliable and efficient, but it is limited to a relatively restricted class of linear Gaussian prob...
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cena:
535,34 zł |