Co-variances were being re-zeroed after being set. This meant that the initial declination learning was sensitive to measurement errors which could result in poor initial yaw accuracy.
Fixes bugs that prevented planes being able to reset yaw to GPS to recovery from takeoff with a bad magnetoemter.
1) If the velocity innovation check had not failed by the time the in-air transition occurred, then the yaw reset would not be performed
2) The velocity states were not being reset
3) The non fly-forward vehicle (copter) reset could occur first and effectively lock out the fly-forward vehicle (plane) yaw check.
Remember the mag bias and earth field states learned during flight when the vehicle lands.
This improves performance for vehicles that do multiple flight on one power cycle
Provide consistent overshoot of 5% across a wider range of time constants and prevent selection of larger time constants causing 'ringing' in the position and velocity outputs.
Fixes a problem observed in a flight log where rapid temperature change caused the accel bias to change faster than the EKF could keep up.
This allows the bias to be learned faster but with acceptable level of noise in the estimate
IMU data was being corrected before being used by the co-variance prediction, whereas the delta angles and velocities in the derivation were supposed to be uncorrected.
This patch creates separate variable for the corrected data
Magnetometer bias states will subject to larger errors early in flight before flight motion makes the offsets observable and the state variances reduce.
Adds a check on state variances.
Replaces the parameter check with a check of the actual filter fusion method being used.
Allow different process noise to be set for body (sensor bias) and earth field states.
This allows a stable magnetometer bias estimate to be available at end of flight whilst still allowing for external magnetic anomalies during landing.
Adjust default values to give stable mag bias learning and fast learning of external anomalies.
Automatically use the highest gain consistent with a 5% overshoot to minimise RMS tracking errors.
Provide an alternative correction method for the position and velocity states that allows the user to specify the time-constant. This can be used to fine tune the output observer for for platform specific sensor errors and control loop sensitivity estimation noise.
The toilet bowling check during early flight has been removed. This check caused problems where bad compass calibration was the cause of the toilet bowling and resetting to the compass was a bad option. The handling of simultaneous failed mag and velocity innovations is already handled outside the EKF by the failsafe.
A check for yaw errors due to a ground based magnetic anomaly has been introduced.
The logic for in-flight yaw and magnetic field resets has been cleaned up and variable names improved.
Splits in-flight yaw alignment completed status into separate yaw and magnetic field flags.
Reduce the number of places where decisions to perform a yaw and field reset are made.
Don't perform a reset unless there is is data in the buffer
Don't use 3-axis fusion if the field states still need to be reset.
When starting 3-axis fusion request a reset if not previously performed.
Ensure magnetometer and GPs heading resets are alwasy perfomred with data at teh correct time horizon.
The commencement of airspeed fusion could cause pitch errors due to small variances and large innovations. This issue is addressed by the following changes:
1) The airspeed measurement is used to set wind states to a value that reduces initial innovations.
2) The wind state variances are set to values that better reflect the wind speed uncertainty
The function used to reset magnetic field states and yaw angle should not be used when there is no magnetometer. If it is incorrectly called without a magnetometer it should not change the attitude or field states.
When changing the vehicle yaw angle, the correlation between the attitude errors and errors in other states is invalid so the corresponding co-variance terms need to be zeroed.
This needs to be done in more than one place.
M_NSE is a measurement noise
P_NSE is a observation noise
I_GATE is an innovation gate
This also ensures the new parameter values required to use the EKF2 will be enforced.
priseborough
Jonathan Challinger
Andrew Tridgell