The central problem in multiple object or target tracking is the data association problem of partitioning sensor reports into tracks of reports and false or unrelated reports. Given a sequence of reports emanating from the same object, the estimation problem is that of using fusion and estimation techniques to improve the estimate of the state of the object. first-year graduate course. The state estimation is confined to the discrete time models as is appropriate for the intended audience. The book is divided into ten chapters. The first six chapters are devoted to the problem of estimation of a single object with a single sensor. Chapter 7 extends the single sensor to multiple sensors and a single object. Chapters 7–10 then consider the full multitarget and multisensory case which includes the association problem. Throughout the estimation chapters 1 through 6, the authors present the estimation formulation from an optimization viewpoint and develop Cramer-Rao bound (CRB) solutions for each estimation problem addressed. The many illustrations provide a deeper understanding of the more difficult concepts, while the exercises help to confirm an understanding of the chapter content and provide a basis for practical usage on existing and new problems. I highly recommend the book as a textbook or a reference for the expert.
Book review: Applied state estimation and association
IEEE Aerospace and Electronic Systems Magazine ; 34 , 1 ; 54-55
01.01.2019
258733 byte
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Book Review: Applied Gas Dynamics
AIAA | 2020
|Book Review: Estimation and Tracking Principles
Online Contents | 1994
|BOOK REVIEWS - Review of Engineering Analysis in Applied Mechanics
Online Contents | 2003
|BOOK REVIEWS - Review of Applied Linear Optimal Control: Examples and Algorithms
Online Contents | 2003
|