Multiperson Continuous Tracking and Identification From mm-Wave Micro-Doppler Signatures

• Published in: IEEE transactions on geoscience and remote sensing Vol. 59; no. 4; pp. 2994 - 3009
• Main Authors: Pegoraro, Jacopo, Meneghello, Francesca, Rossi, Michele
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.04.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Algorithms; Artificial neural networks; Backscattering; Convolutional neural networks; density-based clustering; Doppler radar; Doppler sonar; human tracking; Identification; Indoor environments; indoor monitoring; Kalman filters; Machine learning; micro-Doppler; Millimeter waves; mm-wave radar; multiperson identification; Neural networks; Radar; Radar antennas; Radar tracking; Radio signals; Random noise; Signal processing; Signatures; Spaceborne radar; Target tracking; Tracking
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2020.3019915

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 toward achieving robustness and accuracy in multiuser settings. Our technique is tested both on a single-target public data set, 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.