  Autonomous unmanned aerial vehicle (UAV) inertial navigation exhibits an
extreme dependency on the availability of global navigation satellite systems
(GNSS) signals, without which it incurs in a slow but unavoidable position
drift that may ultimately lead to the loss of the platform if the GNSS signals
are not restored or the aircraft does not reach a location from which it can be
recovered by remote control. This article describes an stochastic high fidelity
simulation of the flight of a fixed wing low SWaP (size, weight, and power)
autonomous UAV in turbulent and varying weather intended to test and validate
the GNSS-Denied performance of different navigation algorithms. Its open-source
\nm{\CC} implementation has been released and is publicly available.
  Onboard sensors include accelerometers, gyroscopes, magnetometers, a Pitot
tube, an air data system, a GNSS receiver, and a digital camera, so the
simulation is valid for inertial, visual, and visual inertial navigation
systems. Two scenarios involving the loss of GNSS signals are considered: the
first represents the challenges involved in aborting the mission and heading
towards a remote recovery location while experiencing varying weather, and the
second models the continuation of the mission based on a series of closely
spaced bearing changes.
  All simulation modules have been modeled with as few simplifications as
possible to increase the realism of the results. While the implementation of
the aircraft performances and its control system is deterministic, that of all
other modules, including the mission, sensors, weather, wind, turbulence, and
initial estimations, is fully stochastic. This enables a robust evaluation of
each proposed navigation system by means of Monte-Carlo simulations that rely
on a high number of executions of both scenarios.
