A novel MCMC tracker for stressing scenarios
Nick Everett, Shien-Shin Tham, David J. Salmond · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2004
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.