The uncertainty team is tasked with achieving things that are unpredictable and very challenging. Of course, things which are certain and easily understood will be handled by other Teams.
We're doing research and implementation of probabilistic algorithms. In particular we are interested in all sorts of filtering: kalman unscented, kalman extended, sigma point kalman, particle and others. These are useful in estimating the position and orientation of a rocket given a diverse array of sensor inputs, all of which are incorrect in their own way. Without such estimation and eventually feeding back this information to our system, controlling a rocket's trajectory can be hugely difficult.
Please join our mailing list, or better yet stop by one of our meetings, we'd love to have your help!
Meetings: The uncertainty team meets weekly with the main group. See the PSAS schedule page for upcoming meetings.
Uncertainty Team Mailing list: psas-avionics
Current projects
- Testing and improving our Bayesian Particle Filtering (BPF) implementation in our simulated rocket environment.
- Flight simulations
Local Resources
- Introduction to the Kalman fiter
- Introduction to state space representations
- Example:INS Aiding and Error Analysis in 1-D
- ActiveGuidance system links and notes.
- Comparison of small orbital vehicles
- Roll Control
More Uncertainty team pages
Tutorials
- Some tutorials, references, and research on the Kalman filter at the Department of Computer Science at the University of North Carolina at Chapel Hill
- Wikipedia article
- Engineers Look to Kalman Filtering for Guidance: Barry Cipra, SIAM News, Vol. 26, No. 5, August 1993
- Kalman Filters at Connexions
- Taygeta's Kalman Filter Information and reading list
- Kalman Filtering, Dan Simon, Innovatia Software
Other Web Links
General introductory material:
- Wikipedia article
- Engineers Look to Kalman Filtering for Guidance: Barry Cipra, SIAM News, Vol. 26, No. 5, August 1993
- Some tutorials, references, and research on the Kalman filter at the Department of Computer Science at the University of North Carolina at Chapel Hill
- Kalman Filters at Connexions
- Taygeta's Kalman Filter Information and reading list
Kalman Filtering, Dan Simon, Innovatia Software
Kalman filter extensions:
- EnKF-The Ensemble Kalman Filter
Other Useful Information:
Haskell resources:
- Matt's DSP library: Modules for matrix manpulation, digital signal processing, spectral stimation and frequency estimation
- HAT: The Haskell Tracer: Source level tracer for ghc and nhc98