A novel MCMC tracker for stressing scenarios

Nick Everett; Shien-Shin Tham; David J. Salmond

doi:10.1117/12.541583

25 August 2004 A novel MCMC tracker for stressing scenarios

Nick Everett, Shien-Shin Tham, David J. Salmond

Proceedings Volume 5428, Signal and Data Processing of Small Targets 2004; (2004) https://doi.org/10.1117/12.541583
Event: Defense and Security, 2004, Orlando, Florida, United States

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.

Citation Download Citation

Nick Everett, Shien-Shin Tham, and David J. Salmond "A novel MCMC tracker for stressing scenarios", Proc. SPIE 5428, Signal and Data Processing of Small Targets 2004, (25 August 2004); https://doi.org/10.1117/12.541583

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available