  We propose Hypernetwork Kalman Filter (HKF) for tracking applications with
multiple different dynamics. The HKF combines generalization power of Kalman
filters with expressive power of neural networks. Instead of keeping a bank of
Kalman filters and choosing one based on approximating the actual dynamics, HKF
adapts itself to each dynamics based on the observed sequence. Through
extensive experiments on CDL-B channel model, we show that the HKF can be used
for tracking the channel over a wide range of Doppler values, matching Kalman
filter performance with genie Doppler information. At high Doppler values, it
achieves around 2dB gain over genie Kalman filter. The HKF generalizes well to
unseen Doppler, SNR values and pilot patterns unlike LSTM, which suffers from
severe performance degradation.
