altos: Correct model error covariance matrix

2013-01-16T23:21:24+00:00

Finally found a couple of decent references on how to set the model (process) error covariance matrix. The current process matrix turns out to be correct for a continuous kalman filter (which isn't realizable, of course). For a discrete filter, the error in modeled acceleration (we model it as a constant) needs to be propogated to the speed and position portions of the matrix. The correct matrix is seen in this paper: On Reduced-Order Kalman Filters For GPS Position Filtering J. Shima 6/2/2001 This references an older paper which is supposed to describe the derivation of the matrix: Singer, R.A., “Estimating Optimal Tracking Filter Performance for Manned Maneuvering Targets,” IEEE Transactions of Aerospace and Electronic Systems, AES-5, July 1970, pp. 473-483. This change has a minor effect on the computed correction coefficients; it should respond more reasonably to acceleration changes now. Signed-off-by: Keith Packard

altos/kalman: Kalman terms can be > 1, use 32-bit fixed point

2011-03-22T07:51:04+00:00

Because speed and acceleration are scaled by 16, it's fairly common for the kalman terms to end up larger than 1. Instead of trying to fuss with 16-bit values and shifts, just use 32-bit values. Signed-off-by: Keith Packard

altos: Add nickle kalman implementation.

2011-03-21T10:59:27+00:00

This generates the constants needed to implement Kalman filtering in the flight firmware. Signed-off-by: Keith Packard

mjb/altos/src/kalman/kalman.5c, branch telemega

altos: Correct model error covariance matrix

altos/kalman: Kalman terms can be > 1, use 32-bit fixed point

altos: Add nickle kalman implementation.