PDF Download Beyond the Kalman Filter: Particle Filters for Tracking Applications (Artech House Radar Library) (Artech House Radar Library (Hardcover))
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Beyond the Kalman Filter: Particle Filters for Tracking Applications (Artech House Radar Library) (Artech House Radar Library (Hardcover))
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About the Author
Branko Ristic is a senior research scientist in the Tracking and Sensor Fusion Group at the ISR Division of DSTO, Edinburgh, Australia. In 2002 he was awarded the Defence Science Fellowship by the Information Sciences Laboratory of DSTO. He earned his Ph.D. at the Signal Processing Research Centre of Queensland University of Technology, Australia.
Sanjeev Arulampalam is a senior research scientist in the Submarine Combat Systems Group, Maritime Operations Division of DSTO, Edinburgh, Australia. In 2000 he was awarded the Anglo-Australian postdoctoral fellowship by the Royal Academy of Engineering, London. He earned his Ph.D. in electrical and electronics engineering at the University of Melbourne, Australia.
Neil Gordon is a senior research scientist in the Tracking and Sensor Fusion Group at the ISR Division of DSTO, Edinburgh, Australia. Dr Gordon earned his Ph.D. in statistics at the Imperial College, University of London.
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Product details
Series: Artech House Radar Library (Hardcover)
Hardcover: 318 pages
Publisher: Artech Print on Demand (January 31, 2004)
Language: English
ISBN-10: 158053631X
ISBN-13: 978-1580536318
Product Dimensions:
6.1 x 0.8 x 9.2 inches
Shipping Weight: 1.2 pounds (View shipping rates and policies)
Average Customer Review:
4.3 out of 5 stars
3 customer reviews
Amazon Best Sellers Rank:
#800,157 in Books (See Top 100 in Books)
I found the first part of this book with the derivation of particle filters and more detailed description of the steps and terms useful. The latter parts with really deep dives into tracking problems wasn't as easily applicable outside of this domain. I wish there was slightly more diversity and range of examples, but then again the words "tracking" is in the title so I should've assumed as much.To my knowledge there are no other really good particle filter texts that have as much detail as this one.
This a really good book for someone who is familiar with the Kalman filter, and wants to learn alternatives, particularly the particle filter. The first section provides a very concise introduction to nonlinear filtering, then a good derivation of the particle filter. The second section shows may different tracking applications that can use a particle filter and discusses different variations of the basic algorithm in relation to the application.
Particle filtering is a powerful method for nonlinear, non-Gaussian state estimation. Having worked in the area for several years, I would say that this book is one of the best introductions available. Unlike other books on the subject which are highly theoretical, this one is steeped in down-to-earth language. Besides giving a very good tutorial overview, it considers a number of practical applications with a separate chapter devoted to each. Studying these help a lot in learning how to apply the filter. I recommend this book highly.
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