A Constant-Gain Equation-Error Framework for Airliner Aerodynamic Monitoring Using QAR Data

Abstract

Monitoring in-service aerodynamic performance of airliners is critical for operational efficiency and safety, yet presents significant challenges when using operational Quick Access Recorder (QAR) data due to sensor noise, low excitation, and the absence of key model parameters like moments of inertia. These constraints render conventional state-propagation and recursive estimation methods unsuitable. To address these challenges, this paper proposes and validates the Constant-Gain Equation-Error Method (CG-EEM), a robust framework tailored for QAR data analysis. The CG-EEM employs a custom constant-gain estimator that avoids both the infeasibility of state-propagation filters and the premature convergence or instability issues of standard recursive algorithms. Extensive validation on a multi-fleet dataset of over 200 flights demonstrates that the framework produces highly consistent and physically meaningful aerodynamic parameters. Crucially, follow-up work has verified that this approach successfully resolves the fundamental thrust-drag ambiguity problem, ensuring the estimates are not just plausible, but physically unique and correct. This demonstrates that CG-EEM is a scalable and computationally efficient tool for reliable fleet-wide performance monitoring and early detection of airframe degradation.