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

Tingwei Zhang; Peng Zhang; Paris Kalathas; Guangxin Wang; Huaping Liu

doi:10.3390/s22176404

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

Sensors (Basel). 2022 Aug 25;22(17):6404. doi: 10.3390/s22176404.

Authors

Tingwei Zhang¹, Peng Zhang², Paris Kalathas¹, Guangxin Wang¹, Huaping Liu¹

Affiliations

¹ School of Electrical Engineering and Computer Science, Oregon State University, Corvallis, OR 97331, USA.
² School of Software & Microelectronics, Peking University, Beijing 100871, China.

Abstract

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.

Keywords: ANN; AOA; RSSI; indoor positioning; machine learning.

MeSH terms

Data Collection
Machine Learning*
Neural Networks, Computer*

Grants and funding

This research received no external funding.