Self-Calibration for the Time Difference of Arrival Positioning.

Area of Science:

• Localization and Navigation
• Signal Processing
• Optimization Techniques

Background:

• Time-difference-of-arrival (TDOA) self-calibration is crucial for applications like indoor navigation.
• Current nonlinear optimization methods often suffer from convergence to local minima, limiting accuracy.

Purpose of the Study:

• To propose a novel dimension lifting method to enhance TDOA self-calibration.
• To mitigate the problem of optimization algorithms getting stuck in local minima.

Main Methods:

• Introduced dimension lifting by adding a variable to the objective function's l 2 norm.
• Developed a partially-analytical method that overdetermines the system of equations.
• Validated the approach using both synthetic and real measurement data.

Main Results:

• The dimension lifting method significantly reduces the likelihood of convergence to local minima.
• Experiments were conducted on DecaWave Ultra-Wideband (UWB) and Abatec Local Position Measurement (LPM) systems.
• Both synthetic and real-world data confirmed the effectiveness of the proposed approach.

Conclusions:

• The proposed dimension lifting technique offers a robust solution for TDOA self-calibration.
• This method improves the reliability of localization systems by avoiding local minima.
• The approach is effective for common systems like UWB and LPM, enhancing indoor navigation accuracy.