Shepherding electrons

Adding accelerometer data to the Kalman filter In my last post I wrote about a Kalman filter to take the MS5611 barometer data and derive both the quadcopter height, and the vertical velocity.  It worked reasonably well but there was a compromise between noise and latency of the filter.  To get even better results, I have incorporated now the accelerometer.  We could produce a new Kalman filter using the height, velocity and acceleration in the state vector, but it turns out we can simply amend our previous filter and include the acceleration in the control vector to improve the predicted state.

Height and vertical velocity from a MS5611 barometer Steps towards vertical flight control This isn't a post explaining how Kalman filters work, I assume some working knowledge.  Instead I wanted to record the process of developing a Kalman filter to derive both the height and the vertical velocity of a quadcopter using a MS5611 barometer, as it may be useful to others.  After recording some flight data on my quadcopter EEPROM, I then spat the output to a PC for post-processing and model training.  In Python I used a Kalman class to generate the following Kalman filter ( Wikipedia: Kalman filter ) based on the barometric height data.