  In this work, we investigate the use of backscattered mm-wave radio signals
for the joint tracking and recognition of identities of humans as they move
within indoor environments. We build a system that effectively works with
multiple persons concurrently sharing and freely moving within the same indoor
space. This leads to a complicated setting, which requires one to deal with the
randomness and complexity of the resulting (composite) backscattered signal.
The proposed system combines several processing steps: at first, the signal is
filtered to remove artifacts, reflections and random noise that do not
originate from humans. Hence, a density-based classification algorithm is
executed to separate the Doppler signatures of different users. The final
blocks are trajectory tracking and user identification, respectively based on
Kalman filters and deep neural networks. Our results demonstrate that the
integration of the last-mentioned processing stages is critical towards
achieving robustness and accuracy in multi-user settings. Our technique is
tested both on a single-target public dataset, for which it outperforms
state-of-the-art methods, and on our own measurements, obtained with a 77 GHz
radar on multiple subjects simultaneously moving in two different indoor
environments. The system works in an online fashion, permitting the continuous
identification of multiple subjects with accuracies up to 98%, e.g., with four
subjects sharing the same physical space, and with a small accuracy reduction
when tested with unseen data from a challenging real-life scenario that was not
part of the model learning phase.
