[D] Can recurrent neural networks perform similar functions as Kalman filters? • r/MachineLearning

I've read that RNNs are particularly well suited for time series predictions, especially equipped with LSTM units that can learn from past data and estimate dependence between instants. This makes me wonder: can RNNs perform estimation like conventional Kalman filters do? For example, in the case of an IMU, typically Kalman filters predict the orientations given raw data from the individual sensors. Would the RNN be able to learn the mapping between raw IMU data and filtered orientations, and predict for future timesteps? If so, it brings me to my second question: would they also be able to'learn' the parameters that model the IMU: such as bias, noise etc.?