@article{(Open Science Index):https://publications.waset.org/pdf/10007158,
	  title     = {Advantages of Neural Network Based Air Data Estimation for Unmanned Aerial Vehicles},
	  author    = {Angelo Lerro and  Manuela Battipede and  Piero Gili and  Alberto Brandl},
	  country	= {},
	  institution	= {},
	  abstract     = {Redundancy requirements for UAV (Unmanned Aerial
Vehicle) are hardly faced due to the generally restricted amount
of available space and allowable weight for the aircraft systems,
limiting their exploitation. Essential equipment as the Air Data,
Attitude and Heading Reference Systems (ADAHRS) require several
external probes to measure significant data as the Angle of Attack
or the Sideslip Angle. Previous research focused on the analysis
of a patented technology named Smart-ADAHRS (Smart Air Data,
Attitude and Heading Reference System) as an alternative method to
obtain reliable and accurate estimates of the aerodynamic angles.
This solution is based on an innovative sensor fusion algorithm
implementing soft computing techniques and it allows to obtain a
simplified inertial and air data system reducing external devices.
In fact, only one external source of dynamic and static pressures
is needed. This paper focuses on the benefits which would be
gained by the implementation of this system in UAV applications.
A simplification of the entire ADAHRS architecture will bring to
reduce the overall cost together with improved safety performance.
Smart-ADAHRS has currently reached Technology Readiness Level
(TRL) 6. Real flight tests took place on ultralight aircraft equipped
with a suitable Flight Test Instrumentation (FTI). The output of
the algorithm using the flight test measurements demonstrates the
capability for this fusion algorithm to embed in a single device
multiple physical and virtual sensors. Any source of dynamic and
static pressure can be integrated with this system gaining a significant
improvement in terms of versatility.},
	    journal   = {International Journal of Aerospace and Mechanical Engineering},
	  volume    = {11},
	  number    = {5},
	  year      = {2017},
	  pages     = {1090 - 1099},
	  ee        = {https://publications.waset.org/pdf/10007158},
	  url   	= {https://publications.waset.org/vol/125},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 125, 2017},