Prevents frame over-runs due to simultaneous fusion of measurements on each instance.
The offset is only applied if less than 5msec available between frames
Vibration in the 400Hz delta angles could cause the angular rate condition check for in-flight magnetic field alignment to fail.
The symptons were failure to start magnetic field learning as expected when EK2_MAG_CAL=3 was set.
Use the more robust, but less accurate compass heading fusion up to 5m altitude
Wait for the magnetometer data fusion time offset to be correct before using data to reset states
Don't reset magnetic field states if the vehicle is rotating rapidly as timing offsets will produce large errors
When doing the yaw angle reset, apply the reset increment to all quaternions stored in the output buffer to avoid transients produced by yaw rotations and the 0.25 second fusion time horizon offset.
Only do the one yaw and mag reset at 5m, not two at 1.5 and 5.0m
Always re-do the yaw and mag reset when leaving the ground.
Now variables don't have to be declared with PROGMEM anymore, so remove
them. This was automated with:
git grep -l -z PROGMEM | xargs -0 sed -i 's/ PROGMEM / /g'
git grep -l -z PROGMEM | xargs -0 sed -i 's/PROGMEM//g'
The 2 commands were done so we don't leave behind spurious spaces.
AVR-specific places were not changed.
The PSTR is already define as a NOP for all supported platforms. It's
only needed for AVR so here we remove all the uses throughout the
codebase.
This was automated with a simple python script so it also converts
places which spans to multiple lines, removing the matching parentheses.
AVR-specific places were not changed.
This increase in assumed altimeter noise and reduction in accel noise has been flight tested by L.Hall with noticeable reduction in the immediate response to Baro errors during moving flight.
This increases the time constant of response to baro errors such that the pilot can more easily compensate.
The previous gain from rate to magnetometer error was excessive. The revised value is equivalent to a magnetic field length of 0.5 with a timing uncertainty of 0.01 sec
Testing on different platforms has shown that the new EKF has smaller innovations enabling innovation consistency checks that reject GPS and baro errors to be tightened.
The position and velocity thresholds for plane have been left the same because planes are less sensitive to GPS glitches as they fly higher and with more separation to surrounding objects. They are also more prone to bad inertial data due to the installation practices.
The altitude noise has been increased on plane to allow for the larger baro disturbances that result from the higher speeds and lack of a proper static pressure source. The innovation consistency gate has been adjusted to provide the same baro error limit of ~20m before baro is rejected.
Extended GPS loss can result in the earth field states becoming rotated and making it difficult for the EKF to recover its heading when GPS is regained.
During prolonged GPS outages, the position covariance can become large enough to cause the reset function to continually activate. This is fixed by ensuring that position covariances are always reset when the position is reset.
The innovation variance was being used incorrectly instead of the state variance to trigger the glitch reset.
Because we have changed the yaw angle and have taken a point sample on the magnetic field, covariances associated with the magnetic field states will be invalid and subsequent innovations could cause an unwanted disturbance in roll and pitch.
The reset of the Euler angles to a new yaw orientation was being done using roll and pitch from the output observer states, not the EKF state vector which meant that when roll and pitch were changing, the reset to a new yaw angle would also cause a roll and pitch disturbance.
This sets the fusion of the synthetic position and velocity to occur at the same time as the barometer
This makes filter tuning more consistent between GPS and non-GPS useage
High measurement data rates can fill buffers with data that is always new and never fused because it is over-written before it falls behind the measurement time horizon.
The legacy EKF switches GPs aiding on on arming, whereas the new EKF switches it on based on GPS data quality.
This means the decision to arm and therefore the predicted solution flags must now reflect the actual status of the navigation solution as it will no longer change when motor arming occurs.
Paul Riseborough
Andrew Tridgell
Lucas De Marchi
Randy Mackay