Beyond The Kalman Filter

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