Push the Limit of Millimeter-wave Radar Localization

Author:

Zhang Guidong1ORCID,Chi Guoxuan1ORCID,Zhang Yi1ORCID,Ding Xuan1ORCID,Yang Zheng1ORCID

Affiliation:

1. Tsinghua University, Beijing, China

Abstract

Existing device-free localization systems have achieved centimeter-level accuracy and show their potential in a wide range of applications. However, today’s radio-based solutions fail to locate the target in millimeter-level due to their limited bandwidth and sampling rate, which constrains their applications in high-accuracy demand scenarios. We find an opportunity to break the bottleneck of existing radio-based localization systems by reconstructing the accurate signal spectral peak from the discrete samples, without changing either the bandwidth or the sampling rate of the radio hardware. This study proposes milliLoc , a millimeter-level radio-based localization system. We first derive a spectral peak reconstruction algorithm to reduce the ranging error from the previous centimeter-level to millimeter-level. Then, we improve the AoA measurement accuracy by leveraging the signal amplitude information. To ensure the practicality of milliLoc , we further extend our system to handle multi-target situations. We fully implement milliLoc on a commercial mmWave radar. Experiments show that milliLoc achieves a median ranging accuracy of 5.5 mm and decreases the AoA measurement error by 31.2% compared with the baseline. Our system fulfills the accuracy requirements of most application scenarios and can be easily integrated with other existing solutions, shedding light on high-accuracy location-based applications.

Funder

National Key Research Plan

NSFC

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Networks and Communications

Reference49 articles.

1. Sandeep Rao. 2020. Introduction to mmWave Sensing: FMCW Radars. Retrieved from https://training.ti.com/sites/default/files/docs/mmwaveSensing-FMCW-offlineviewing.pdf.

2. Livox Technology. 2020. Livox Mid-40 LiDAR. Retrieved from https://www.livoxtech.com/mid-40-and-mid-100.

3. Texas Instruments. 2020. Texas Instruments DCA1000EVM. Retrieved from https://www.ti.com/tool/DCA1000EVM.

4. Texas Instruments. 2020. Texas Instruments IWR1443BOOST. Retrieved from https://www.ti.com/tool/IWR1443BOOST.

5. Fadel Adib, Zachary Kabelac, and Dina Katabi. Multi-person localization via RF body reflections. In Proceedings of USENIX NSDI.

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