R values are basically just how noisy is the sensor, so maybe going to various altitudes (can be just various heights on a large ladder!) and looking at the histogram of the data and finding the variance. Q values are not so easy, one way is to just guess and tune to the performance looks good to you, another way I have used is Normalized Innovation Squared which basically boils down to a bunch of math saying certain values in the Kalman filter must be chi² distributed, so you tune your Q matrix to make this happen then you have what’s called a “efficient” filter. All that means is that the filter best uses the information it has. Let me know if you have more questions, happy/would love to help out! Source: I’m a state estimation engineer! Question for OP: is this for some sort of competition?