🤖 AI Summary
This study addresses the lack of quantitative criteria for selecting between closed-loop and open-loop Kalman filter architectures in airborne aided inertial navigation. To this end, the authors propose a unified simulation framework to systematically evaluate the performance differences of these two error-state Kalman filtering approaches under varying inertial sensor accuracy levels. The methodology employs standard inertial mechanization in the geodetic frame combined with direct position aiding, enabling a comprehensive analysis of the trade-off between fusion smoothness and long-term stability. The work provides the first quantitative evidence that, with high-accuracy IMUs, the open-loop architecture yields smoother state fusion, whereas the closed-loop configuration offers superior long-term navigation stability, thereby offering empirical guidance for filter architecture selection.
📝 Abstract
Closed-loop (or feedback) error-state Kalman filters with their relatives and offspring are the state-of-the-art in modern aided inertial navigation research. Estimated inertial navigation system (INS) errors are continually fed back to the INS to correct the nominal system state before subsequent predictions. Conversely, in safety-critical aeronautical applications, open-loop (or feedforward) systems are an undisputed standard, where the inertial mechanization is strictly decoupled to allow for operational independence and fault isolation of computing units. We assess the performance impacts of this architectural choice beyond qualitative system-safety justifications using a standard inertial mechanization in geodetic coordinates and direct position aiding. Simulations using a variety of inertial sensor error characteristics, ranging from consumer to navigation grade systems, showcase the trade-off between smooth information fusion for high-end IMUs using an open-loop filter and the inherent long-term stability of the closed-loop architecture.