[Bayes++] Square-root UKF

[Matt H. <mwh...@gm...>]

Is there any support for Square-root (cholesky decomposition, etc)
formulations of the Unscented Kalman filter in Bayes++? I'm working on a
GPS/INS system purposed for real-time control of a helicopter UAV; any
computational cost reduction on the estimation end could directly lead to a
performance gain in the controller.

Also, what is the best way to organize the state and measurement vectors? I
hope to use the UKF to estimate the bias and scale-factor errors present in
the signals from 3-axis rate gyros, 3-axis accelerometers, 3-axis
magnetometers, GPS, and barometric altitude/airspeed sensors. Of course,
only the sensor measurements are directly observable (the bias/sf errors
can't be measured) - any suggestions on the best way to represent this in
the observation implementation?

Right now I'm just working on getting the correct predict/observe cycle. I'm
using an Addative_predict_model and an Uncorrelated_addative_observe_model.
Are there underlying assumptions for these models that make them less
suitable than the base model (Unscented_scheme::observe, for instance?)

Thanks for your advice,

Matt Hazard
NCSU Aerial Robotics Club
http://art1.mae.ncsu.edu
North Carolina State University