A self-learning mean optimization filter to improve bluetooth 5.1 AoA indoor positioning accuracy for ship environments

• Published in: Journal of King Saud University. Computer and information sciences Vol. 35; no. 3; pp. 59 - 73
• Main Authors: Lin, Qianfeng, Son, Jooyoung, Shin, Hyeongseol
• Format: Journal Article
• Language: English
• Published: Saudi Arabia Elsevier B.V 01.03.2023; The Author(s). Published by Elsevier B.V. on behalf of King Saud University; Elsevier
• Subjects: Angle of arrival; Bluetooth; Indoor positioning; Ship environments; Angle of arrival; Bluetooth; Ship environments; Indoor positioning
• ISSN: 1319-1578; 2213-1248; 1319-1578
• DOI: 10.1016/j.jksuci.2023.01.019

•We propose a novel parameter self-learning estimation approach for localization.•We present an optimal average self-learning solution for dynamic ship environments.•The developed filter can be used for the indoor-positioning in the ship environments. As COVID-19 is still spreading globally, the narrow ship space makes COVID-19 easier for the virus to infect ship passengers. Tracking close contacts remains an effective way to reduce the risk of virus transmission. Therefore, indoor positioning technology should be developed for ship environments. Today, almost all smart devices are equipped with Bluetooth. The Angle of Arrival (AoA) using Bluetooth 5.1 indoor positioning technology is well suited for ship environments. But the narrow ship space and steel walls make the multipath effect more pronounced in ship environments. This also means that more noises are included in the signal. In the Uniform Rectangular Array (URA) type receiving antenna array, elevation and azimuth angles are two important parameters for the AoA indoor positioning technology. Elevation and azimuth angles are unstable because of the influence of noise. In this paper, a Self-Learning Mean Optimization Filter (SLMOF) is proposed. The goal of SLMOF is to find the optimal elevation and azimuth angles as a way to improve the Bluetooth 5.1 AoA indoor positioning accuracy. The experimental results show that the Root Mean Square Error (RMSE) of SLMOF is 0.44 m, which improves the accuracy by 72% compared to Kalman Filter (KF). This method can be applied to find the optimal average in every dataset.