An Improved BLE Indoor Localization with Kalman-Based Fusion: An Experimental Study

Area of Science:

• Computer Science
• Electrical Engineering
• Robotics

Background:

• Indoor positioning systems are crucial but lack universal efficacy.
• Existing methods like trilateration and dead reckoning have limitations in accuracy and stability.
• High-accuracy indoor localization remains a significant challenge in various applications.

Purpose of the Study:

• To propose a novel indoor positioning method by fusing trilateration and dead reckoning.
• To enhance positioning accuracy by incorporating environmental context information.
• To evaluate the performance of the proposed fusion method against traditional approaches.

Main Methods:

• Developed a fusion algorithm using Kalman filtering for position estimation.
• Utilized Android devices with Bluetooth Low Energy (BLE) for stable signal strength and low energy consumption.
• Integrated environmental context into the positioning algorithm to refine accuracy.

Main Results:

• The proposed fusion method significantly outperformed standalone trilateration and dead reckoning.
• Kalman-based fusion achieved an indoor positioning accuracy of less than one meter in experimental settings.
• The incorporation of environmental context further improved the positioning accuracy.

Conclusions:

• The novel fusion method offers a robust and accurate solution for indoor positioning.
• Kalman filtering effectively integrates multiple positioning techniques for enhanced performance.
• The system demonstrates practical applicability for precise indoor localization using BLE technology.