  mmWave radars have recently gathered significant attention as a means to
track human movement within indoor environments. Widely adopted Kalman filter
tracking methods experience performance degradation when the underlying
movement is highly non-linear or presents long-term temporal dependencies. As a
solution, in this article we design a convolutional-recurrent Neural Network
(NN) that learns to accurately estimate the position and the velocity of the
monitored subjects from high dimensional radar data. The NN is trained as a
probabilistic model, utilizing a Gaussian negative log-likelihood loss
function, obtaining explicit uncertainty estimates at its output, in the form
of time-varying error covariance matrices. A thorough experimental assessment
is conducted using a 77 GHz FMCW radar. The proposed architecture, besides
allowing one to gauge the uncertainty in the tracking process, also leads to
greatly improved performance against the best approaches from the literature,
i.e., Kalman filtering, lowering the average error against the ground truth
from 32.8 to 7.59 cm and from 56.8 to 14 cm/s in terms of position and velocity
tracking, respectively.
