# Filterpy Kalman Filter batch processing with multiple measurement sources

In pythons module for kalman-filtering, filterpy, there is a function batch_filter() to batch filter a list of measurements that then can be used for RTS-smoothing. See the documentation here.

I want to smooth my measurements with a RTS-smoother like:

(mu, cov, _, _) = kf.batch_filter(list(np.array(centroids)))
(x, P, K, Pp) = kf.rts_smoother(mu, cov)


The problem is now that I have two measurements from two different sources with different measurement noises. The function batch_filter() can only process one source of measurements. When I calculate and save x and P for every time step with the sequence "predict, update with R1, update with R2", is this the same thing batch_filter() would do?