ULoc

计算机科学 可扩展性 测距 网络数据包 实时计算 功率消耗 延迟(音频) 带宽(计算) 计算机网络 功率(物理) 电信 量子力学 数据库 物理
作者
Minghui Zhao,Tyler A. Chang,Aditya Arun,Roshan Ayyalasomayajula,Chi Zhang,Dinesh Bharadia
出处
期刊:Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies [Association for Computing Machinery]
卷期号:5 (3): 1-31 被引量:35
标识
DOI:10.1145/3478124
摘要

A myriad of IoT applications, ranging from tracking assets in hospitals, logistics, and construction industries to indoor tracking in large indoor spaces, demand centimeter-accurate localization that is robust to blockages from hands, furniture, or other occlusions in the environment. With this need, in the recent past, Ultra Wide Band (UWB) based localization and tracking has become popular. Its popularity is driven by its proposed high bandwidth and protocol specifically designed for localization of specialized "tags". This high bandwidth of UWB provides a fine resolution of the time-of-travel of the signal that can be translated to the location of the tag with centimeter-grade accuracy in a controlled environment. Unfortunately, we find that high latency and high-power consumption of these time-of-travel methods are the major culprits which prevent such a system from deploying multiple tags in the environment. Thus, we developed ULoc, a scalable, low-power, and cm-accurate UWB localization and tracking system. In ULoc, we custom build a multi-antenna UWB anchor that enables azimuth and polar angle of arrival (henceforth shortened to '3D-AoA') measurements, with just the reception of a single packet from the tag. By combining multiple UWB anchors, ULoc can localize the tag in 3D space. The single-packet location estimation reduces the latency of the entire system by at least 3×, as compared with state of art multi-packet UWB localization protocols, making UWB based localization scalable. ULoc's design also reduces the power consumption per location estimate at the tag by 9×, as compared to state-of-art time-of-travel algorithms. We further develop a novel 3D-AoA based 3D localization that shows a stationary localization accuracy of 3.6 cm which is 1.8× better than the state-of-the-art two-way ranging (TWR) systems. We further developed a temporal tracking system that achieves a tracking accuracy of 10 cm in mobile conditions which is 4.3× better than the state-of-the-art TWR systems.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
大幅提高文件上传限制,最高150M (2024-4-1)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
刚刚
红尘踏歌发布了新的文献求助10
1秒前
1秒前
陌路孤星发布了新的文献求助30
1秒前
1秒前
JK157完成签到,获得积分10
2秒前
2秒前
3秒前
4秒前
阳光发布了新的文献求助10
4秒前
5秒前
5秒前
希望天下0贩的0应助Ashley采纳,获得10
5秒前
Yuxuan发布了新的文献求助10
5秒前
下颌磨牙钳完成签到,获得积分10
6秒前
Liar应助JK157采纳,获得10
7秒前
隐形曼青应助ayee采纳,获得10
7秒前
恶恶么v发布了新的文献求助10
7秒前
8秒前
9秒前
liangkai发布了新的文献求助10
9秒前
科研通AI2S应助余歌采纳,获得10
9秒前
leoskrrr发布了新的文献求助10
9秒前
善学以致用应助yuyu采纳,获得10
9秒前
sasaki完成签到,获得积分10
10秒前
lyouang完成签到,获得积分10
10秒前
北世发布了新的文献求助10
11秒前
11秒前
瑾璟发布了新的文献求助20
12秒前
12秒前
顾矜应助苹果骑士采纳,获得10
12秒前
12秒前
情怀应助机智的瑾瑜采纳,获得10
13秒前
13秒前
14秒前
艺术家完成签到 ,获得积分10
14秒前
阳光完成签到,获得积分10
14秒前
小马甲应助努力摆烂采纳,获得10
16秒前
Hello应助冷酷的格尔曼采纳,获得10
16秒前
16秒前
高分求助中
Sustainability in Tides Chemistry 2000
Bayesian Models of Cognition:Reverse Engineering the Mind 800
Essentials of thematic analysis 700
A Dissection Guide & Atlas to the Rabbit 600
Very-high-order BVD Schemes Using β-variable THINC Method 568
Внешняя политика КНР: о сущности внешнеполитического курса современного китайского руководства 500
Revolution und Konterrevolution in China [by A. Losowsky] 500
热门求助领域 (近24小时)
化学 医学 生物 材料科学 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 基因 遗传学 催化作用 物理化学 免疫学 量子力学 细胞生物学
热门帖子
关注 科研通微信公众号,转发送积分 3124336
求助须知:如何正确求助?哪些是违规求助? 2774637
关于积分的说明 7723368
捐赠科研通 2430117
什么是DOI,文献DOI怎么找? 1290937
科研通“疑难数据库(出版商)”最低求助积分说明 621972
版权声明 600297