Using the Fingerprinting Method to Customize RTLS Based on the AoA Ranging Technique.

Area of Science:

• Engineering
• Computer Science
• Signal Processing

Background:

• Real-time Locating Systems (RTLSs) are crucial for applications requiring precise object and personnel tracking.
• Existing RTLS solutions often face limitations in localization precision, impacting their effectiveness in critical areas like security and healthcare.

Purpose of the Study:

• To develop an analytical method for customizing RTLSs to improve localization precision.
• To investigate the influence of indoor space geometry, RTLS arrangement, and sensor placement on localization accuracy.

Main Methods:

• Utilized Angle of Arrival (AoA) ranging, a fingerprinting method, and an analytically defined AoA uncertainty.
• Developed a localization uncertainty map incorporating workspace geometry and RTLS arrangement.
• Employed a statistical approach to assess localization precision for sensor pairs using AoA signals.

Main Results:

• Validated the analytical fingerprinting map model through simulations and physical experiments.
• Demonstrated that workspace geometry and layout significantly impact RTLS performance.
• Showcased how workspace size, shape, and calibration point placement affect the fingerprint map.

Conclusions:

• The analytically established fingerprint map accurately represents RTLS precision.
• Optimized sensor arrangement is critical for achieving high localization precision.
• The proposed method provides a framework for customizing RTLS for specific environments and applications.