author = {King, Thomas Horton and Soltanaghai, Elahe and Prabhakara, Akarsh and Balanuta, Artur and Kumar, Swarun and Rowe, Anthony},
title = {Long-Range Accurate Ranging of Millimeter-Wave Retro-Reflective Tags in High Mobility},
year = {2021},
isbn = {9781450383424},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3447993.3510592},
doi = {10.1145/3447993.3510592},
abstract = {In this paper, we demonstrate Adaptive Millimetro as an extension of Millimetro, an ultra-low power millimeter-wave (mmWave) retro-reflector presented in [1], for high mobility scenarios. Adaptive Millimetro makes use of automotive radars and enables communication with and accurate localization of roadside infrastructure overextended distances (i.e. >100m). Millimetro achieves this by designing ultra-low-power retro-reflective tags that operate in the mmWave frequency band and can be embedded in road signs, pavements, bi-cycles, or even the clothing of pedestrians. Millimetro addresses the severe path loss problem of mmWave signals by combining coding gain and retro-reflective antenna front-end to achieve long-range operation. However, highly mobile scenarios may still experience unreliable performance due to the Doppler effect changing the received signals. In this paper, we demonstrate a simple solution for robust localization in high mobility by implementing a Moving Target Indication (MTI) filter and an adaptive Kalman filter. We also present an augmented reality app, as an in-car AR platform, that uses Adaptive Millimetro’s algorithms to estimate the tag positions and overlay a virtual box at the estimated locations.},
booktitle = {Proceedings of the 27th Annual International Conference on Mobile Computing and Networking},
pages = {883–884},
numpages = {2},
location = {New Orleans, Louisiana},
series = {MobiCom '21}