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author | Keith Packard <keithp@keithp.com> | 2013-01-16 15:01:12 -0800 |
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committer | Keith Packard <keithp@keithp.com> | 2013-01-16 15:21:24 -0800 |
commit | dd60d85d07b881ac03294a8cf607e469f2e69610 (patch) | |
tree | 255e3d9b4fc65b10c140551d685f84ec6461a138 /micropeak/MicroStatsTable.java | |
parent | 994ff76a064dcbd3113db771cd9cd9591fd68dea (diff) |
altos: Correct model error covariance matrix
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 <keithp@keithp.com>
Diffstat (limited to 'micropeak/MicroStatsTable.java')
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