  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.
