PmTrack

IF 3.6 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Hankai Liu, Xiulong Liu, Xin Xie, Xinyu Tong, Keqiu Li
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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.
PmTrack
难以获得目标身份是实现个性化和定制毫米波(mmWave)传感的一大障碍。从信号特征中学习个体差异的现有解决方案在实际应用中存在局限性。本文通过引入惯性测量单元(IMU)作为身份指示器,提出了基于毫米波的个性化人体跟踪系统 PmTrack。惯性测量单元广泛应用于智能手表和智能手机等便携式设备,利用现有的无线网络上传身份和数据,因此能够以轻便的方式协助雷达目标识别,而且用户的部署和携带负担很小。PmTrack 创新性地采用了方位作为匹配特征,从而很好地克服了雷达和 IMU 之间的数据异质性,同时避免了累积误差的影响。在 PmTrack 的实现过程中,我们在检测增强、干扰抑制、连续性保持和轨迹校正等方面提出了一整套优化方法,成功解决了多人跟踪中弱反射、点云重叠和人体弹跳鬼影三大难题所带来的一系列实际问题。此外,还提出了克服 IMU 万向节锁定的方向校正方法。大量实验结果表明,PmTrack 在大厅和会议室中的五人识别准确率分别达到了 98% 和 95%。
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来源期刊
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Computer Science-Computer Networks and Communications
CiteScore
9.10
自引率
0.00%
发文量
154
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