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

Area of Science:

• Maritime technology
• Wireless communication
• Signal processing

Background:

• COVID-19 transmission is a significant risk in confined ship environments.
• Accurate indoor positioning is vital for effective contact tracing and disease control at sea.
• Existing Bluetooth 5.1 Angle of Arrival (AoA) positioning faces challenges due to multipath effects and noise in ship settings.

Purpose of the Study:

• To develop an advanced filtering method for improving Bluetooth 5.1 AoA indoor positioning accuracy in ship environments.
• To address the instability of elevation and azimuth angles caused by signal noise.
• To enhance the reliability of indoor positioning for maritime health and safety applications.

Main Methods:

• Implementation of a Self-Learning Mean Optimization Filter (SLMOF) algorithm.
• Utilizing Bluetooth 5.1 Angle of Arrival (AoA) technology with a Uniform Rectangular Array (URA) antenna.
• Testing and comparison against the Kalman Filter (KF) for performance evaluation.

Main Results:

• The proposed SLMOF achieved a Root Mean Square Error (RMSE) of 0.44 m.
• SLMOF demonstrated a 72% improvement in positioning accuracy compared to the Kalman Filter (KF).
• The filter effectively optimizes elevation and azimuth angles, reducing noise influence.

Conclusions:

• The Self-Learning Mean Optimization Filter (SLMOF) significantly enhances Bluetooth 5.1 AoA indoor positioning accuracy in challenging ship environments.
• This technology offers a viable solution for precise contact tracing and risk mitigation of infectious diseases on vessels.
• The SLMOF method's ability to find optimal averages in datasets provides a robust approach for improving wireless positioning systems.