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

Human Tracking with mmWave Radars: a Deep Learning Approach with Uncertainty Estimation