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I would suggest looking at "On the Identification of Variances and Adaptive Kalman Filtering" by R. Mehra as a starting point. This paper is highly cited and the methods are not difficult to implement.


You have to calculate the error in rad. Other then that, I would always use gaussian error propagation, unless there is a special reason.


If you want the worst case error, the error in each term is at most $\pm0.005$, so the error in the total is within $\pm 0.005 n$. If $n$ is large, you can use the normal approximation to say the error is roughly normal with zero mean and standard deviation $0.005 \cdot \frac {\sqrt n}2$

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