Beyond the Kalman Filter Particle Filters for Tracking Applications

This book PDF is perfect for those who love Technology & Engineering genre, written by Branko Ristic and published by Artech House which was released on 01 December 2003 with total hardcover pages 328. You could read this book directly on your devices with pdf, epub and kindle format, check detail and related Beyond the Kalman Filter Particle Filters for Tracking Applications books below.

Beyond the Kalman Filter  Particle Filters for Tracking Applications
Author : Branko Ristic
File Size : 42,9 Mb
Publisher : Artech House
Language : English
Release Date : 01 December 2003
ISBN : 1580538517
Pages : 328 pages
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Beyond the Kalman Filter Particle Filters for Tracking Applications by Branko Ristic Book PDF Summary

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.

Beyond the Kalman Filter  Particle Filters for Tracking Applications

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

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