BIM-AKF: A hybrid approach to robust INS vertical channel stabilization

• A novel BIM-AKF framework with AAS support is proposed for the INS VC stabilization. • VC errors are modeled using scalar GM/sinusoidal stochastic processes via ACF/AV tools. • The proposed approach is experimentally validated using real UAV flight navigation data. Vertical Channel (VC) instability poses a critical challenge in Inertial Navigation Systems (INSs), particularly for aerial applications. This work proposes a novel hybrid architecture that integrates Baro-Inertial Mechanization (BIM), as a pre-stabilizer for the INS VC, with an Augmented Kalman Filter (AKF) that models only the residual VC errors after BIM correction. Such an architecture is therefore referred to as BIM-AKF. Unlike standard Kalman Filters (KFs), which embed the full unstable INS VC dynamics, BIM-AKF leverages either Auto-Correlation Function (ACF) or Allan Variance (AV) analysis of BIM-only outputs to optimally parameterize residual error propagation in state-space. The framework seamlessly supports Additional Aiding Sensors (AASs), e.g., Global Navigation Satellite Systems (GNSSs). Real Unmanned Aerial Vehicle (UAV) flights with intentional GNSS outages demonstrate that the proposed BIM-AKF outperforms existing methods, establishing a new benchmark in robust multi-sensor integrated navigation.