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.
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.
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.
Eliminate the use of horizontal position states during non-aiding operation to make it easier to tune.
Explicitly set the horizontal position associated Kalman gains to zero and the coresponding covariance entries to zero after avery fusion operation.
Make the horizontal velocity observation noise used during non-aiding operation adjustable.
Use a fixed value of velocity noise during initial alignment so that the flight peformance can be tuned without affecting the initial alignment.
The ad-hoc scaling of error growth has been replaced with a consistent method that uses the main nav filters published vertical velocity uncertainty and the terrain gradient assumption.
Down-sample the IMU and output observer state data to 100Hz for storage in the buffer.
This reduces storage requirements for Copter by 75% or 6KB
It does not affect memory required by plane which already uses short buffers due to its 50Hz execution rate.
This means that the EKF filter operations operate at a maximum rate of 100Hz.
The output observer continues to operate at 400Hz and coning and sculling corrections are applied during the down-sampling so there is no loss of accuracy.
Only applied to interfaces required for data logging.
If an invalid instance is requested, the data for the primary instance is returned. This allows the primary data to be returned by calling with a -1 instance value.
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
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.
This parameter is a compromise between numerical accuracy of the covariance prediction and sensor timing jitter
Further testing has shown that doing covariance prediction and sensor fusion every 10msec has no observable effect on fusion health and reduces timing hitter noise on magnetometer observations during high rate maneovures
The values chosen ensure that up to consistent 250 msec of sensor delay compensation is available for different platform types
The revised values also ensure that fusion occurs at different time to when the 10Hz magnetometer measurements are read
Provide an option to always do learning
Make field learning decision logic clearer
Change defaults so that plane learns when airborne
Change defaults so that Rover does not learn (large external magnetic interference)
priseborough
Jonathan Challinger
Tom Pittenger
Ricardo de Almeida Gonzaga
Andrew Tridgell
Don Gagne
Siddharth Bharat Purohit
Paul Riseborough
Lucas De Marchi
Randy Mackay