Person Tracking in Ultra-Wide Band Hybrid Localization System Using Reduced Number of Reference Nodes.

Area of Science:

• Engineering
• Computer Science
• Robotics

Background:

• Indoor localization systems often struggle with accuracy in environments with limited reference nodes or signal interference.
• Hybrid systems combining different sensing modalities offer potential for improved robustness and precision.

Purpose of the Study:

• To present a novel method for integrating positional data from inertial navigation and radio distance measurements in an indoor hybrid localization system.
• To address the challenge of unambiguous localization with fewer than three reference nodes and in areas with disturbed radio signal propagation.

Main Methods:

• Development of a hybrid localization system demonstrator combining inertial navigation and radio distance measurements.
• Integration of positional data using a novel method to compensate for inertial sensor drift.
• Utilizing distance measurements to fewer than three reference nodes to continuously provide localization services.

Main Results:

• The proposed method reduced localization error by nearly 50% compared to using inertial navigation alone.
• Achieved a long-term root mean square error of approximately 0.50 m.
• Localization precision degradation was below 0.1 m relative to fusion Kalman filtration with four reference nodes.

Conclusions:

• The novel integration method enables continuous localization services even with limited reference nodes and disturbed radio signal propagation.
• The hybrid approach significantly improves position estimation accuracy in indoor environments.
• The system demonstrates a practical solution for robust and precise indoor positioning.