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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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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.
- Sales Rank: #894154 in Books
- Published on: 2004-01-31
- Original language: English
- Number of items: 1
- Dimensions: 9.21" h x .75" w x 6.14" l, 1.20 pounds
- Binding: Hardcover
- 318 pages
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.
Most helpful customer reviews
4 of 4 people found the following review helpful.
Good book
By G. L. Sinsley
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.
3 of 3 people found the following review helpful.
Excellent Intro to Particle Filtering
By Ernest Ohlmeyer
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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