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.


    Access

    Download


    Export, share and cite



    Title :

    Book review: Applied state estimation and association


    Contributors:


    Publication date :

    2019-01-01


    Size :

    258733 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English