This paper presents a heading alignment procedure for drone navigation employing a single hover GNSS antenna combined with low-grade MEMs-IMU sensors. The design was motivated by the need for a drone-mounted differential interferometric SAR (DinSAR) application. Still, the methodology proposed here applies to any Unmanned Aerial Vehicle (UAV) application that requires high-precision navigation data for short-flight missions utilizing cost-effective MEMs sensors. The method proposed here involves a Bayesian parameter estimation based on a simultaneous cumulative Mahalanobis metric applied to the innovation process of Kalman-like filters, which are identical except for the initial heading guess. The procedure is then generalized to multidimensional parameters, thus called parametric alignment, referring to the fact that the strategy applies to alignment problems regarding some parameters, such as the heading initial value. The motivation for the multidimensional extension in the scenario is also presented. The method is highly applicable for cases where gyro-compassing is not available. It employs the most straightforward optimization techniques that can be implemented using a real-time parallelism scheme. Experimental results obtained from a real UAV mission demonstrate that the proposed method can provide initial heading alignment when the heading is not directly observable during takeoff, while numerical simulations are used to illustrate the extension to the multidimensional case.
Real-Time and Post-Mission Heading Alignment for Drone Navigation Based on Single-Antenna GNSS and MEMs-IMU Sensors
2025
Article (Journal)
Electronic Resource
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