A Modular Windows-Based Intelligent API for Traceable Drone Positioning Using UWB-OptiTrack Fusion and AI-Based Residual Learning

Ihtisham Ul Haq, Luigi D’Alfonso, Giuseppe Fedele, Francesco Lamonaca

Abstract

Accurate and traceable drone positioning is crucial for autonomous aerial navigation, especially in GNSS-denied environments. Traditional approaches using Ultra-Wideband (UWB) sensors or Kalman Filters (KF) struggle with multipath interference, non-line-of-sight, and environmental uncertainties, and are often limited to Linux-based Application Programming Interfaces (APIs). This work presents an innovative framework based on: a novel modular Windows-based API for real-time drone positioning, the integration of Kalman filter for optimal multi-sensor data fusion and trajectory smoothing, AI-driven residual learning to correct systematic estimation errors, and metrology-compliant uncertainty modeling. The system enables real-time swarm deployment and pose-aware feedback using an auxiliary vision based positioning system (OptiTrack) and UWB data. A feedforward neural network compensates for residual errors in Kalman-filtered trajectories, while Monte Carlo simulations establish traceable 95% confidence intervals. Experimental tests show that the proposed framework reduces RMSE by over 40% across axes, with strong regression accuracy greater than 94%.

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IMEKO-TC6-2025-061.pdf

DOI

10.21014/tc6-2025.061

IMEKO TC

TC6 - Digitalization

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