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?
I am not use how you can success multiple sensors using batch filter, but, in sequential filtering you need to perform time update before each measurement. You can do it using the timetag information of each sensor measurement. You can modify the batch filter code however, I strongly recommend to write your own function depending on what you need.