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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| Vydáno v: | Proceedings of the IEEE National Radar Conference (1996) s. 1 - 6 |
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| Jazyk: | angličtina |
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21.09.2020
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| ISSN: | 2375-5318 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Solimini, Domenico Rossi, Michele Pegoraro, Jacopo Matteo, Federico Bashirov, Enver Meneghello, Francesca |
| Author_xml | – sequence: 1 givenname: Jacopo surname: Pegoraro fullname: Pegoraro, Jacopo email: pegoraroja@dei.unipd.it organization: University of Padova,Department of Information Engineering – sequence: 2 givenname: Domenico surname: Solimini fullname: Solimini, Domenico organization: University of Padova,Department of Mathematics – sequence: 3 givenname: Federico surname: Matteo fullname: Matteo, Federico organization: University of Padova,Department of Mathematics – sequence: 4 givenname: Enver surname: Bashirov fullname: Bashirov, Enver organization: University of Padova,Department of Information Engineering – sequence: 5 givenname: Francesca surname: Meneghello fullname: Meneghello, Francesca organization: University of Padova,Department of Information Engineering – sequence: 6 givenname: Michele surname: Rossi fullname: Rossi, Michele organization: University of Padova,Department of Information Engineering |
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| Snippet | 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... |
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| SubjectTerms | 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 |
| Title | Deep Learning for Accurate Indoor Human Tracking with a mm-Wave Radar |
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