Deep Learning for Accurate Indoor Human Tracking with a mm-Wave Radar
| Autor: | Michele Rossi, Domenico Solimini, Jacopo Pegoraro, Francesca Meneghello, Federico Matteo, Enver Bashirov |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
denoising autoencoders
indoor sensing Computer science sequence-to-sequence autoencoders Noise reduction 02 engineering and technology Tracking (particle physics) law.invention Extended Kalman filter 0203 mechanical engineering law Computer vision Radar mm-wave radar 020301 aerospace & aeronautics Ground truth Radar tracker Artificial neural network business.industry Deep learning mm-wave radar indoor sensing human tracking denoising autoencoders sequence-to-sequence autoencoders 020302 automobile design & engineering Trajectory Artificial intelligence human tracking business |
| Zdroj: | 2020 IEEE Radar Conference (RadarConf20) |
| DOI: | 10.1109/radarconf2043947.2020.9266400 |
| Popis: | 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. This conference paper is subject to ©2020 IEEE Published version of the paper can be found in the proceedings of RadarConf2020. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|