research-article

LSAB: Enhancing Spatio-temporal Efficiency of AoA Tracking Systems

Authors:
Qingrui Pan

Department of Computing, The Hong Kong Polytechnic University, Hong Kong SAR

Department of Computing, The Hong Kong Polytechnic University, Hong Kong SAR

0000-0003-3902-3878
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,
Zhenlin An

Department of Computing, The Hong Kong Polytechnic University, Hong Kong SAR

Department of Computing, The Hong Kong Polytechnic University, Hong Kong SAR

0000-0003-4120-773X
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,
Lei Yang

Department of Computing, The Hong Kong Polytechnic University, Hong Kong SAR

Department of Computing, The Hong Kong Polytechnic University, Hong Kong SAR

0000-0001-9651-0811
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,
Qiongzheng Lin

Cainiao Network, China

Cainiao Network, China

0000-0001-5804-4012
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Authors Info & Claims

ACM Transactions on Sensor Networks Volume 18 Issue 4Article No.: 58pp 1–25https://doi.org/10.1145/3534123

Published:29 November 2022Publication History

ACM Transactions on Sensor Networks

Abstract

Estimating the angle-of-arrival (AoA) of an RF source by using a large-sized antenna array is a classical topic in wireless systems. However, AoA tracking systems are not yet used for Internet of Things (IoT) in the real world due to their unaffordable cost. Many efforts, such as a time-sharing array, emulated array, and sparse array, were recently made to cut the cost. This work introduces a log-spiral antenna belt (LSAB), a new novel sparse “planar array” that could estimate the AoA of an IoT device in 3D space by using a few antennas connected to a single timeshare channel. Unlike the conventional arrays, LSAB deploys antennas on a log-spiral-shaped belt in a non-linear manner, following the theory of minimum resolution redundancy newly discovered in this work. One physical 8 × 8 uniform planar array (UPA) and four logical sparse arrays, including LSAB, were prototyped to validate the theory and evaluate the performance of sparse arrays. The extensive benchmark demonstrates that the performance of LSAB was comparable to that of a UPA, with similar degree of resolution; and LSAB could provide over 40% performance improvement than existing sparse arrays. We also prototyped a second LSAB adapted to an RFID system for localizing RFID tags at centimeter-level accuracy.

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Index Terms

LSAB: Enhancing Spatio-temporal Efficiency of AoA Tracking Systems
1. Networks
  1. Network services
    1. Location based services

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Published in
ACM Transactions on Sensor Networks Volume 18, Issue 4
November 2022
619 pages
ISSN:1550-4859
EISSN:1550-4867
DOI:10.1145/3561986
Editor:
Yunhao Liu
Tsinghua University, China
Issue’s Table of Contents
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
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Publication History
- Published: 29 November 2022
- Online AM: 26 May 2022
- Accepted: 27 April 2022
- Received: 25 February 2022
Published in tosn Volume 18, Issue 4

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antenna array
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LSAB: Enhancing Spatio-temporal Efficiency of AoA Tracking Systems

ACM Transactions on Sensor Networks

Abstract

REFERENCES

Cited By

Index Terms

Recommendations

LSAB: Enhancing Spatio-Temporal Efficiency of AoA Tracking Systems

Performance Analysis of 3D Localization for a Launch Vehicle Using TOA, AOA, and TDOA

Performance Enhancement of Hybrid TDOA/AOA Using Multipath Delay Estimation

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LSAB: Enhancing Spatio-temporal Efficiency of AoA Tracking Systems

ACM Transactions on Sensor Networks

Abstract

REFERENCES

Cited By

Index Terms

Recommendations

LSAB: Enhancing Spatio-Temporal Efficiency of AoA Tracking Systems

Performance Analysis of 3D Localization for a Launch Vehicle Using TOA, AOA, and TDOA

Performance Enhancement of Hybrid TDOA/AOA Using Multipath Delay Estimation

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