Beyond the Kalman Filter: Particle Filters Fro 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. This cutting-edge book introduces the latest advances in particle filter theory, discusses their relevance to defence surveillance systems, and examines defence-related applications of particle filters to nonlinear and non-Gaussian problems. 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 manoeuvering targets, range-only tracking, terrain-aided tracking of ground vehicles, and group and extended object tracking.

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. <P>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. <P>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.