Research on phase-aware uncertainty quantification for UAVs based on bayesian inference and polynomial chaos expansion
Year: 2026
Type: article
Abstract: The flight process of unmanned aerial vehicles (UAVs) is influenced by various uncertain parameters, such as air density, aerodynamic coefficients, and propulsion system thrust. Prior to mission execution, predicting the flight envelope under the impact of uncertainties is critical, and uncertainty quantification (UQ) serves as the key method to address this challenge. Traditionally, in UAV UQ analysis, unknown parameters are often assigned specific distributions based on prior knowledge. However, such priors may be subjective and are frequently set overly conservatively to meet safety margins. Conversely, historical flight data holds immense value for quantifying uncertainties in reusable or identical-model UAVs. This paper proposes a method that utilizes historical flight data to estimate parameters through Bayesian inference, then fuses these estimates with prior knowledge to establish more objective distributions for uncertain parameters. Reasonable parameter distributions positively impact UQ by preventing control strategies from being insufficiently robust or overly redundant. Therefore, this paper explores uncertainty quantification for UAVs under different information sources and accelerates the algorithm using Gaussian Process Regression (GPR) and Polynomial Chaotic Expansion (PCE).
Authors: Shi Qiu, Zhao Rui Li, Huixian Sun, Baofeng Guo
Institution: PLA Army Engineering University
Language: English
Subfield: Computational Mechanics
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7140172011