On Wed, Dec 19, 2012 at 10:54 AM, James Turner <zakal...@mac.com> wrote:
> Right - but I think exposing the values without gravity added in is
> useful, and the for the internal (C++) value, the Yasim one doesn't include
> gravity, so neither should then JSBsim value (both FDMs compute both
> variants already!). Then we could start exposing linear accelerations over
> MP (or a future MP system) and make the prediction better. (And obviously
> that prediction won't want to include gravitational terms)
>
I won't complain if you wish to publish a new set of values (that don't
include gravity) under a new set of property names. I assume it would make
the most sense to use the NED coordinate frame (not the body frame) for
gravity-less acceleration values.
BTW, is your Kalman filtering code available anywhere? It might be of
> interest :)
It's from a university project and I've never gotten the green light to
make the source available -- although I've asked a few times. Overall, it
is an interesting math problem. Given gps (geodetic position and velocity
in the NED coordinate frame) along with inertial sensor data in the body
coordinate frame (gyro, and accelerometers which does include gravity), can
you then compute the actual orientation of your vehicle (roll, pitch, yaw.)
It turns out you can solve for a correction transform (a rotation) that
makes your gps agree with your inertials, but there are infinite solutions
(it's an under-constrained problem). But if you take the data across
multiple time steps, you can apply some least squares techniques to come up
with a "best fit" solution. The kalman filter can take that a step further
and estimate your sensor biases so you can do better project-ahead between
gps readings, and well it's a kalman filter which is sort of obscure magic.
Curt.
--
Curtis Olson:
http://www.atiak.com - http://aem.umn.edu/~uav/
http://www.flightgear.org - http://gallinazo.flightgear.org
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