@inproceedings{a16d6faa6fa84693a76dc068c66e20ed,
title = "Fusang: Graph-inspired Robust and Accurate Object Recognition on Commodity mmWave Devices",
abstract = "This paper presents the design and implementation of Fusang, a low-barrier system that brings accurate and robust 3D object recognition to Commercial-Off-The-Shelf mmWave devices. The basic idea of Fusang is leveraging the large bandwidth of mmWave Radars to capture a unique set of fine-grained reflected responses generated by object shapes. Moreover, Fusang constructs two novel graph-structured features to robustly represent the reflected responses of the signal in the frequency domain and IQ domain, and carefully designs a neural network to accurately recognize objects even in different multipath scenarios. We have implemented a prototype of Fusang on a commodity mmWave Radar device. Our experiments with 24 different objects show that Fusang achieves a mean accuracy of 97\% in different multipath environments. The code, dataset, and trained models of Fusang can be obtained at https://github.com/OpenNISLab/Pro-Fusang.",
keywords = "HRRP, graph-inspired feature, mmwave radar, object recognition",
author = "Guorong He and Shaojie Chen and Dan Xu and Xiaojiang Chen and Yaxiong Xie and Xinhuai Wang and Dingyi Fang",
note = "Publisher Copyright: {\textcopyright} 2023 Owner/Author(s).; 21st ACM International Conference on Mobile Systems, Applications, and Services, MobiSys 2023 ; Conference date: 18-06-2023 Through 22-06-2023",
year = "2023",
month = jun,
day = "18",
doi = "10.1145/3581791.3596849",
language = "English",
series = "MobiSys 2023 - Proceedings of the 21st Annual International Conference on Mobile Systems, Applications and Services",
publisher = "Association for Computing Machinery, Inc",
pages = "489--502",
booktitle = "MobiSys 2023 - Proceedings of the 21st ACM International Conference on Mobile Systems, Applications and Services",
}