Examination of Drone Localization Performance with Commercially Available Embedded GPS Sensors

Abstract

With the commercial drone market worth 24 billion USD and project to grow, accurate 3D localization in urban air mobility is critical. Existing GPS systems in these environments are plagued by multipath propagation and signal obstruction, resulting in typical urban GPS errors ranging from 15 to 100 meters, far below the precision needed for safe operations. Using a combination of experimental testing and data-driven modeling, this study quantitatively demonstrates that a composite machine learning approach using commercially available embedded GPS sensors, incorporating a Windowed Inverse Variance Weighted Filter combined with LSTM Recurrent Neural Networks, can significantly reduce localization errors. Testing across varied urban settings, GPS sensors improved location accuracy by up to 47% compared to conventional filter methods in literature, with the model effectively reducing the average error in urban environments to less than 1.8 meters. Dynamic Accuracy Index (DAI), a metric quantifying the balance between positional accuracy and data processing speed, is introduced. The experimental results reveal our system's proficiency, particularly evident in its DAI of 0.18 meter/Hz, surpassing other conventional filter methods operating at approximately 1 Hz with a DAI of 5.11 meter/Hz and proposed filter + neural network methods presented in the literature with DAI of 2.17. The findings affirm the potential of using recurrent neural network-based machine learning algorithms in enhancing GPS localization systems, presenting a viable pathway for integrating these technologies into commercial UAV operations. This contribution is not only pivotal for the advancement of urban air mobility but also enhances the safety and operational efficiency of UAVs in complex environments.