Deep Learning for Accurate Indoor Human Tracking with a mm-Wave Radar

We address the use of backscattered mm-wave radio signals to track humans as they move within indoor environments. The common approach in the literature leverages the extended Kalman filter (EKF) method, which however undergoes a severe performance degradation when the system evolution model is high...

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Bibliographic Details
Published in:Proceedings of the IEEE National Radar Conference (1996) pp. 1 - 6
Main Authors: Pegoraro, Jacopo, Solimini, Domenico, Matteo, Federico, Bashirov, Enver, Meneghello, Francesca, Rossi, Michele
Format: Conference Proceeding
Language:English
Published: IEEE 21.09.2020
Subjects:
denoising autoencoders
Extraterrestrial measurements
human tracking
indoor sensing
mm-wave radar
Neural networks
Noise reduction
Radar tracking
sequence-to-sequence autoencoders
Spaceborne radar
Target tracking
Trajectory
ISSN:2375-5318
Online Access:Get full text
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Summary:We address the use of backscattered mm-wave radio signals to track humans as they move within indoor environments. The common approach in the literature leverages the extended Kalman filter (EKF) method, which however undergoes a severe performance degradation when the system evolution model is highly non-linear or presents long-term time dependencies among the system states. In this work, we propose an original model-free tracking procedure based on denoising autoencoders and sequence-to-sequence neural networks, showing its superior performance with respect to state-of-the-art methods. Our architecture can be trained in either a supervised or unsupervised manner, trading tracking accuracy for flexibility. The proposed system is tested on our own measurements, obtained with a 77 GHz radar on single and multiple subjects simultaneously moving in an indoor space. The results are compared against the ground truth trajectories from a motion tracking system, obtaining average tracking errors as low as 12 cm.
ISSN:2375-5318
DOI:10.1109/RadarConf2043947.2020.9266400