Beyond The Kalman Filter
This hands-on guide helps professionals develop more accurate and reliable non-linear filter designs and more precisely predict the performance of these designs. Practitioners can also apply particle filters to tracking a ballistic object, detection and tracking of stealthy targets, tracking through the blind Doppler zone, bistatic radar tracking, passive ranging (Bearings-only tracking) of maneuvering targets, range-only tracking, terrain-aided tracking of ground vehicles, and group and...
Search in google:
The fundamental building block of a target tracking radar system is the filter for recursive target state estimation, with the Kalman filter being the best-known example. The authors of this work (all of Australia's Defense Science and Technology Organization) believe that particle filters relying on sequential Monte Carlo estimation and non-Gaussian dynamic estimation are growing to be more useful than Kalman filters. Writing for engineers, they review the current status of nonlinear/non-Gaussian filtering and describe common techniques. They then turn their attention to an array of target tracking applications, most of which rely on particle filters. Annotation ©2004 Book News, Inc., Portland, OR
PrefaceAcknowledgmentsITheoretical Concepts1Ch. 1Introduction3Ch. 2Suboptimal Nonlinear Filters19Ch. 3A Tutorial on Particle Filters35Ch. 4Cramer-Rao Bounds for Nonlinear Filtering67IITracking Applications83Ch. 5Tracking a Ballistic Object on Reentry85Ch. 6Bearings-Only Tracking103Ch. 7Range-Only Tracking153Ch. 8Bistatic Radar Tracking179Ch. 9Tracking Targets Through the Blind Doppler203Ch. 10Terrain-Aided Tracking215Ch. 11Detection and Tracking of Stealthy Targets239Ch. 12Group and Extended Object Tracking261Epilogue287AppCoordinate Transformations for Tracking289List of Acronyms293About the Authors295Index297
