Weighted Least-Squares Bluetooth Localization Method Based on Data Preprocessing

Ling‐Feng Shi

doi:10.1109/JIOT.2024.3445134

2024-12-01

PRE

DOI：10.1109/JIOT.2024.3445134

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摘要

En 中文

People's need for positioning and navigational data has grown over time as communication technology has advanced, becoming an essential and significant aspect of daily life. As a result of the global navigation satellite systems (GNSSs) increasing maturity, satellite positioning technology can now achieve the highest centimeter-level positioning accuracy, effectively meeting the majority of outdoor positioning requirements. However, indoor positioning technology emerged as a solution for locations where satellite signals are obstructed and cannot normally provide positioning information. Researchers have focused on the Bluetooth positioning algorithm among them because it is a wireless positioning method. There are two types of traditional Bluetooth received signal strength indicator (RSSI) positioning methods based on the Internet of Things (IoT): 1) trilateral and 2) multilateral. However, because environmental interference causes the Bluetooth signal's RSSI value to fluctuate greatly, using either of the above methods will result in significant errors and a decline in positioning accuracy when buildings, objects, or people are in the way. To address this issue, this article proposes preprocessing the data prior to applying the least-squares method. At the same time, based on processing outcomes, the weighted least-squares method is utilized to determine Bluetooth RSSI positioning, resulting in a notable improvement over the accuracy of the traditional RSSI Bluetooth positioning.

Keyword:

Bluetooth

Wireless communication

Accuracy

Mathematical models

Internet of Things

Global navigation satellite system

Satellites

indoor positioning

least-squares method

received signal strength indicator (RSSI)