Share Email Print

Proceedings Paper

A novel MCMC tracker for stressing scenarios
Author(s): Nick Everett; Shien-Shin Tham; David J. Salmond
Format Member Price Non-Member Price
PDF $17.00 $21.00

Paper Abstract

We propose a very generic Bayesian framework for the principled exploitation of probabilistic batch-learning technologies for real-time state estimation. To illustrate our concepts, we derive a nonlinear filtering/smoothing solution for a challenging case study in target tracking. We also demonstrate the application of Markov chain Monte Carlo (MCMC) sampling methods as a computational tool within our framework. Finally, we present simulation results, benchmarked against a comparable particle filter.

Paper Details

Date Published: 25 August 2004
PDF: 12 pages
Proc. SPIE 5428, Signal and Data Processing of Small Targets 2004, (25 August 2004); doi: 10.1117/12.541583
Show Author Affiliations
Nick Everett, QinetiQ Ltd. (United Kingdom)
Shien-Shin Tham, QinetiQ Ltd. (United Kingdom)
David J. Salmond, QinetiQ Ltd. (United Kingdom)

Published in SPIE Proceedings Vol. 5428:
Signal and Data Processing of Small Targets 2004
Oliver E. Drummond, Editor(s)

© SPIE. Terms of Use
Back to Top
Sign in to read the full article
Create a free SPIE account to get access to
premium articles and original research
Forgot your username?