PmTrack

Author:

Liu Hankai1ORCID,Liu Xiulong1ORCID,Xie Xin1ORCID,Tong Xinyu1ORCID,Li Keqiu1ORCID

Affiliation:

1. Tianjin University, Tianjin, China

Abstract

The difficulty in obtaining targets' identity poses a significant obstacle to the pursuit of personalized and customized millimeter-wave (mmWave) sensing. Existing solutions that learn individual differences from signal features have limitations in practical applications. This paper presents a Personalized mmWave-based human Tracking system, PmTrack, by introducing inertial measurement units (IMUs) as identity indicators. Widely available in portable devices such as smartwatches and smartphones, IMUs utilize existing wireless networks for data uploading of identity and data, and are therefore able to assist in radar target identification in a lightweight manner with little deployment and carrying burden for users. PmTrack innovatively adopts orientation as the matching feature, thus well overcoming the data heterogeneity between radar and IMU while avoiding the effect of cumulative errors. In the implementation of PmTrack, we propose a comprehensive set of optimization methods in detection enhancement, interference suppression, continuity maintenance, and trajectory correction, which successfully solved a series of practical problems caused by the three major challenges of weak reflection, point cloud overlap, and body-bounce ghost in multi-person tracking. In addition, an orientation correction method is proposed to overcome the IMU gimbal lock. Extensive experimental results demonstrate that PmTrack achieves an identification accuracy of 98% and 95% with five people in the hall and meeting room, respectively.

Funder

National Natural Science Foundation of China

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Networks and Communications,Hardware and Architecture,Human-Computer Interaction

Reference81 articles.

1. MiShape: Accurate Human Silhouettes and Body Joints from Commodity Millimeter-Wave Devices;Adhikari Aakriti;Proc. ACM IMWUT,2022

2. OmniScatter

3. We Hear Your PACE: Passive Acoustic Localization of Multiple Walking Persons;Cai Chao;Proc. ACM IMWUT,2021

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