Qiu, K.; Chen, R.; Wu, Y.; Guo, G.; Li, W. Robust Bluetooth AOA Estimation for Indoor Localization Using Particle Filter Fusion. Preprints2024, 2024051684. https://doi.org/10.20944/preprints202405.1684.v1
APA Style
Qiu, K., Chen, R., Wu, Y., Guo, G., & Li, W. (2024). Robust Bluetooth AOA Estimation for Indoor Localization Using Particle Filter Fusion. Preprints. https://doi.org/10.20944/preprints202405.1684.v1
Chicago/Turabian Style
Qiu, K., Guangyi Guo and Wei Li. 2024 "Robust Bluetooth AOA Estimation for Indoor Localization Using Particle Filter Fusion" Preprints. https://doi.org/10.20944/preprints202405.1684.v1
Abstract
With the growing demand for positioning services, angle-of-arrival (AoA) estimation or direction-finding (DF) has been widely investigated for applications in fifth-generation (5G) technologies. Many existing AoA estimation algorithms only require the measurement of the direction of the incident wave at the transmitter to obtain correct results. However, for most of the cellular systems such as Bluetooth indoor positioning system due to multipath and non-line-of-sight (NLOS) propagation, the indoor positioning accuracy is severely affected. In this paper a comprehensive algorithm is investigated that combines radio measurements from Bluetooth AoA local navigation systems with indoor position estimates obtained using particle filtering, which allows us to explore new optimised methods to reduce estimation errors in indoor positioning. First, particle filtering is used to predict the rough position of a moving target. Then, an algorithm with robust beam weighting is used to estimate the AoA of the multipath components. Based on this, a system of pseudo-linear equations for target positioning based on the probabilistic framework of PF and AoA measurement is derived. Theoretical analysis and simulation results show that the algorithm can improve the positioning accuracy by about 25.7% on average.
Keywords
Indoor Localization; particle filter (PF); angle of arrival (AoA); Bluetooth direction finding; robust beam weighting
Subject
Environmental and Earth Sciences, Remote Sensing
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
