  In this paper we adapt KalmanNet, which is a recently pro-posed deep neural
network (DNN)-aided system whose architecture follows the operation of the
model-based Kalman filter (KF), to learn its mapping in an unsupervised manner,
i.e., without requiring ground-truth states. The unsupervised adaptation is
achieved by exploiting the hybrid model-based/data-driven architecture of
KalmanNet, which internally predicts the next observation as the KF does. These
internal features are then used to compute the loss rather than the state
estimate at the output of the system. With the capability of unsupervised
learning, one can use KalmanNet not only to track the hidden state, but also to
adapt to variations in the state space (SS) model. We numerically demonstrate
that when the noise statistics are unknown, unsupervised KalmanNet achieves a
similar performance to KalmanNet with supervised learning. We also show that we
can adapt a pre-trained KalmanNet to changing SS models without providing
additional data thanks to the unsupervised capabilities.
