A Systematic Review and Comparison of Calibration Techniques for UWB Localization Anchors

Ultra-wideband (UWB) systems are critical for indoor positioning in robotics, industrial tracking, and asset man-agement due to their accuracy in multipath-prone environments. Like GPS satellites requiring precise orbital data, UWB systems depend on well-calibrated anchors—fixed reference points whose positional accuracy directly impacts location estimates. We systematically evaluate and compare computational calibration methods, such as Genetic Algorithms, Maximum Likelihood, and the Extended Kalman Filter, using synthetic data, assessing both efficiency and error reduction in calibration and location. Nonlinear Least Squares (NLS) outperformed other approaches from this review as well as state-of-the-art methods, reducing anchor calibration errors to 10.7cm (86.03% improvement from 1-meter initial uncertainty) and tag localization errors to 5.6cm (88.35% reduction). NLS maintained computational efficiency (mean execution time of 0.011s, proving ideal for real-world deployments where efficiency and accuracy are critical.