# Applying Neural Networks in Aerial Vehicle Guidance to Simplify Navigation Systems
## 5 Conclusions  
A novel methodology, which depends on an innovative hybridization among several sensor signals, has been proposed. At the core of the approach, neural networks are employed to get estimations of the gravity vector, which allows avoiding the use of gyroscopes. Traditional GNSS/IMU frameworks feature little errors of up to one meter, which may imply huge mistakes in line of sight vector computation when separation to the objective is small. With the proposed approach the exactness of line of sight calculation can be improved during the terminal GNC, enhancing the accuracy at the destination point, while sensor needs are lowered.  
The proposed approach employs information gathered from GNSS, accelerometers, and a semiactive laser kit. With that information, two different neural network architectures are applied to estimate the gravity vector in order to determine the attitude of the vehicle. Three training algorithms have been addressed to tune the parameters in the neural networks. In total, six different strategies are developed for estimating the gravity vector along the trajectory. In addition, because the methodology allows for determining attitude in two ways, the information is hybridized with the aim of augmenting precision.  
This innovative methodology is integrated into a two-phase guidance algorithm for aerial vehicles, which provides the required input data for the GNC system. The guidance law is founded on a constant glide angle and on a modified proportional law. The control algorithm is based on a robust and effective but simple double-input double-output device. Overall, the resulting GNC system presents excellent values regarding dispersion at the destination objective, significantly increasing the precision for noncontrolled flights, as expected, but also matching accuracy as provided by other GNC systems requiring more sensors on-board. Note that results also show good behavior of the system under uncertainty conditions. Summarizing, the developed approach, which is based on neural networks, shows that precision levels can be matched or improved as compared to other methodologies.  
Future research will address the increase of the use of neural networks in other modules of GNC algorithms to further simplify the overall architecture.  
Conceptualization, R.d.C.; methodology, R.d.C. and L.C.; software, L.C.; validation, R.d.C.; formal analysis, R.d.C. and P.S.; investigation, R.d.C. and P.S.; resources, L.C.; data curation, R.d.C. and P.S.; writing-original draft preparation, L.C.; writing-review and editing, R.d.C.; visualization, R.d.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Project Grant F663--AAGNCS by the \"Direccion General de Investigacion e Innovacion Tecnologica, Consejeria de Ciencia, Universidades e Innovacion, Comunidad de Madrid\" and \"Universidad Rey Juan Carlos\".  
The authors would like to thank Lieutenant Colonel [PERSON] (NMT) of the National Institute for Aerospace Technology (INTA) for the solid modeling of the concept.  
The authors declare no conflict of interest.