Improvement of Ultra-Wideband-Based 3D Localization for Indoor Drones Using Anchor Auto Calibration and Positioning Based on Machine Learning

3D localization is an important parts of indoor drone navigation systems. 3D localization using ultra-wideband (UWB) can be applied to solve the GPS-denied environment problem. However, UWB has some disadvantages related to measurement accuracy, measurement fluctuation, and position estimation in 3-dimensional space. The UWB measurement accuracy is affected by the antenna delay. The antenna delay must be calibrated for each anchor before measurement is performed. Performing an automatic calibration for antenna delay can significantly increase the consistency and efficiency of measurement systems. Conventional localization methods, such as trilateration, triangulation, or multilateration are effectively proven in 2-dimensional localization. This method produced significant error while performing 3-dimensional localization, especially in Z-axis. A new approach based on ML CNN is expected to solve the complexity and nonlinearity in data measurement that arise in conventional methods. Data fluctuation problems due to UWB measurement during the position estimation process result in large estimation errors. A motion threshold is implemented after position estimation to solve these problems. Position changes that are significantly greater than the maximum drone velocity limit can be eliminated. Based on the experimental results, the implementation of AAC in ML-based 3D localization with a motion threshold significantly increased the positioning accuracy up to 0.34 m, lowered the standard deviation up to 0.12 m, and eliminated the outliers caused by data fluctuation with a maximum of 1.07 m.

3D localization; Anchor auto-calibration; Indoor drone; Machine learning; Motion threshold; Ultra-wideband

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