# How to use the covariance matrix of the robot_localization package

I am trying to fuse IMU and GPS odometry using the ekf_robot_localization node. I would like the filtered odometry to be more dependent on the IMU and less on the GPS as the noise of the GPS is quite visible in the filtered output. I figured that adapting the covariance matrix will be able to give me this result by increasing the variation of the GPS measurements. However, i cannon find any information on what each variable in the covariance matrix relates to. Could someone explain the function of each variable in the covariance matrix and how to let the filtered output be more dependent of a specific sensor?

@someDutchguy any follow up on this?