Minimization of GNSS-Denied Inertial Navigation Errors for Fixed Wing Autonomous Unmanned Air Vehicles
This addresses navigation challenges for low SWaP autonomous UAVs in GNSS-denied environments, but it is incremental as it builds on existing sensor fusion techniques.
The paper tackles GNSS-denied inertial navigation for fixed-wing UAVs by proposing a filter that uses onboard sensors like magnetometers and Pitot tubes to minimize attitude error and position drift, achieving reduced errors in simulations compared to traditional methods.
This article proposes an inertial navigation algorithm intended to lower the negative consequences of the absence of GNSS (Global Navigation Satellite System) signals on the navigation of autonomous fixed wing low SWaP (Size, Weight, and Power) UAVs (Unmanned Air Vehicles). In addition to accelerometers and gyroscopes, the filter takes advantage of sensors usually present onboard these platforms, such as magnetometers, Pitot tube, and air vanes, and aims to minimize the attitude error and reduce the position drift (both horizontal and vertical) with the dual objective of improving the aircraft GNSS-Denied inertial navigation capabilities as well as facilitating the fusion of the inertial filter with visual odometry algorithms. Stochastic high fidelity Monte Carlo simulations of two representative scenarios involving the loss of GNSS signals are employed to evaluate the results, compare the proposed filter with more traditional implementations, and analyze the sensitivity of the results to the quality of the onboard sensors. The author releases the C++ implementation of both the navigation filter and the high fidelity simulation as open-source software.