A Machine Learning Approach to Improve Ranging Accuracy with AoA and RSSI

被引：4

作者：

Zhang, Tingwei ^{[1
]}

Zhang, Peng ^{[2
]}

Kalathas, Paris ^{[1
]}

Wang, Guangxin ^{[1
]}

Liu, Huaping ^{[1
]}

机构：

[1] Oregon State Univ, Sch Elect Engn & Comp Sci, Corvallis, OR 97331 USA

[2] Peking Univ, Sch Software & Microelect, Beijing 100871, Peoples R China

来源：

SENSORS | 2022年 / 22卷 / 17期

关键词：

machine learning; ANN; AOA; RSSI; indoor positioning; PULSED UWB SYSTEMS; LOCALIZATION; MULTIPATH;

D O I：

10.3390/s22176404

中图分类号：

O65 [分析化学];

学科分类号：

070302 ; 081704 ;

摘要：

Ranging accuracy is a critical parameter in time-based indoor positioning systems. Indoor environments often have complex structures, which make centimeter-level-accurate ranging a challenging task. This study proposes a new distance measurement method to decrease the ranging error in multipath environment. Our method uses an artificial neural network that utilizes the received signal strength indicator along with a signal's angle of arrival to calculate the line-of-sight distance. This combination results in a significant reduction of the error caused by multipath effects that common RSSI-based methods suffer from. It outperforms traditional ranging methods while the implementation complexity is kept low.

引用

页数：10

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