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...

Ausführliche Beschreibung

Bibliographische Detailangaben

Veröffentlicht in:	Proceedings of the IEEE National Radar Conference (1996) S. 1 - 6
Hauptverfasser:	Pegoraro, Jacopo, Solimini, Domenico, Matteo, Federico, Bashirov, Enver, Meneghello, Francesca, Rossi, Michele
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	IEEE 21.09.2020
Schlagworte:	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-Zugang:	Volltext
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