ReBEL is a Matlab® toolkit of functions and scripts, designed to facilitate sequential Bayesian inference (estimation) in general state space models. This software consolidates research on new methods for recursive Bayesian estimation and Kalman filtering by Rudolph van der Merwe and Eric A. Wan at the OGI School of Science & Engineering at OHSU (Oregon Health & Science University).
ReBEL currently contains most of the following functional units which can be used for state-, parameter- and joint-estimation:

• Kalman filter
• Extended Kalman filter
• Sigma-Point Kalman filters (SPKF)
  • Unscented Kalman filter (UKF)
  • Central difference Kalman filter (CDKF)
  • Square-root SPKFs
  • Gaussian mixture SPKFs
  • Iterated SPKF
  • SPKF smoothers
• Particle filters
  • Generic SIR particle filter
  • Gaussian sum particle filter
  • Sigma-point particle filter
  • Gaussian mixture sigma-point particle filter
  • Rao-Blackwellized particle filters

The italicized algorithms above are not fully functional yet (or included in the current release), but will be in the next or future releases. The code is designed to be as general, modular and extensible as possible, while at the same time trying to be as computationally efficient as possible. It has been tested with Matlab 7.2 (R2006a).
10/11/2006 : New bug-fix release available: ReBEL 0.2.7. Fixed some small bugs, rolled in user contributed patches and added m2html documentation.

Acknowledgements: This software consolidates research on new methods for recursive Bayesian estimation and Kalman filtering and is supported in part by the NSF under contract ECS-0083106, DARPA under contract F33615-98-C-3516 and ONR under contract N0014-02-C-0248.