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.
The new function can deal with a variable number of function parameters.
Additionally, I renamed the functions to norm(), because this is the
standard name used in several other projects.
Implements the following techniques to enable planes to operate without magnetometers.
1) When on ground with mag use inhibited, a synthetic heading equal to current heading is fused to prevent uncontrolled covariance growth.
2) When transitioning to in-flight, the delta between inertial and GPS velocity vector is used to align the yaw.
3) The yaw gyro bias state variance is reset following an in-flight heading reset to enable the yaw gyro bias to be learned faster.
Use an Euler yaw heading that switches between a 321 and 312 rotation
sequence to avoid areas of singularity. Using Euler yaw decouples the
observation from the roll and pitch states and prevents magnetic
disturbances from affecting roll and pitch via the magnetometer fusion
process.
The use of yaw angle fusion during startup and ground operation causes problems with tail-sitter vehicle types.
Instead of observing an Euler yaw angle, we now observe the yaw angle obtained by projecting the measured magnetic field onto the the horizontal plain.
This avoids the singularities associated with the observation of Euler yaw angle.
The innovation calculation should have been updated when the heading fusion maths was updated.
We now use a direct heading or yaw angle measurement in the derivation, not the difference between observed and published declination.
This removes a legacy design concept that is no longer required in this filter implementation. Planes will not be armed without EKF aiding and the proposed copter throw mode also requires EKF aiding to be operating.
The other problem with interrupting fusion during the launch is it doesn't reduce the corrections, it just delays them as wen the launch completes, the EKF inertial position estimate is still moving still moved and the corrections are therefore just delayed by the short launch interval.
Thank you to OXINARF for picking up the inconsistency with the previous logic
Change to user adjustable fusion of constant position (as per legacy EKF) instead of constant velocity.
Enable user to specify use of 3-axis magnetometer fusion when operating without aiding.
Don't allow gyro scale factor learning without external aiding data as it can be unreliable
Sensor bias corrections were being applied to the incoming IMU data using the wrong delta time.
This was what was driving the different tuning between plane and copter for gyro bias process noise so the same gyro bias process noise default tuning value can now be used for all platform types.
Sensor bias corrections were being applied a a second time to the output observer inertial data.
priseborough
Jonathan Challinger
Andrew Tridgell
Tom Pittenger
Ricardo de Almeida Gonzaga
dgrat
Lucas De Marchi
Peter Barker