A Hybrid Method to Improve the BLE-Based Indoor Positioning in a Dense Bluetooth Environment

Area of Science:

• Electrical Engineering
• Computer Science
• Signal Processing

Background:

• Bluetooth Low Energy (BLE) beacons are widely used for indoor positioning.
• Dense Bluetooth environments present challenges due to complex and fluctuating signal propagation.
• Existing methods struggle with high received signal strength indication (RSSI) variation and long data collection intervals in crowded BLE settings.

Purpose of the Study:

• To address the performance degradation of BLE indoor positioning in dense Bluetooth environments.
• To propose a novel hybrid method for enhanced indoor positioning accuracy and timeliness.
• To evaluate the effectiveness of the proposed fusion method against traditional approaches.

Main Methods:

• A hybrid method combining sliding-window filtering, trilateration, and dead reckoning.
• Utilizing Kalman filtering to integrate trilateration and dead reckoning data.
• Conducting extensive experiments in a real-world implementation to compare positioning approaches.

Main Results:

• The proposed fusion method demonstrated superior performance in dense Bluetooth environments.
• Significant improvements in positioning accuracy and timeliness were observed.
• The hybrid approach effectively reduced positioning errors, achieving real-time positioning capabilities.

Conclusions:

• The hybrid fusion method is highly effective for BLE-based indoor positioning in dense environments.
• Kalman filtering plays a crucial role in merging different positioning techniques for better results.
• The developed approach offers a robust solution for accurate and timely indoor positioning.