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I am working on fusing GPS and IMU sensor measurement to calculate position in x and y direction. In this process I am not able to figure out how to calculate Q and R matrix values for kalman filtering.

Some details of implementation

  • GPS coordinate are converted from geodetic to local NED coordinates
  • IMU sensor data (Accelerometer, Gyroscope and Magnetometer) values are fused using AHRS filter to get roll, pitch and yaw angles
  • IMU sensor measurement (acceleration) is converted from body coordinate to NED coordinate using rotation matrix

I am getting IMU and GPS sensor values from my mobile phone. I am stuck at a point getting a method to calculate correct Q and R matrix values.

As of now, I am using constant values Q = diag[0.001,0.001] and R = diag[2000000,4000000] (this looks weird) using these values I am getting somewhat fused position estimates. When value of R is set to low value for example [10,20] the estimated position follows exactly GPS coordinates, there is no influence of IMU sensor measurements.

I trying to figure out why high value for measurement noise matrix works, I assume since I am converting GPS coordinate from geodetic to local NED values, so the need for higher values. But anyway I am sure this is not the right way to calculate this.

It would be very helpful if someone can help me out.

Thank you

babyCoder
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1 Answers1

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(this is a comment but SO forced me to write an answer)

As you've discovered, in most cases it is the ratio of R to Q that determines the estimator behavior. Can you share the Kalman filter time-update equations, measurement-update equations, and the sampling time? Those will say something about the units of Q and R.

imuengine.io
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