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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. Packed with 867 equations, this cutting-edge book introduces the latest advances in particle filter theory, discusses their relevance to defense surveillance systems, and examines defense-related applications of particle filters to nonlinear and non-Gaussian problems.
With this hands-on guide, you can develop more accurate and reliable nonlinear filter designs and more precisely predict the performance of these designs. You can also apply particle filters to tracking a ballistic object, detection and tracking of stealthy targets, tracking through the blind Doppler zone, bi-static radar tracking, passive ranging (bearings-only tracking) of maneuvering targets, range-only tracking, terrain-aided tracking of ground vehicles, and group and extended object tracking.
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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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