# Applying Neural Networks in Aerial Vehicle Guidance to Simplify Navigation Systems
## 1 Introduction  
Global Navigation Satellite Systems (GNSS) signals are widely utilized for aerospace applications. However, reliability decreases as the requirement of the application for which it is designed increases. The main cause producing this effect is the reduced signal/noise relationship caused by the attenuation and loss of the GNSS signal. This basically means that independent sources of data for navigation are needed to ameliorate these negative effects and reduce interference. Inertial Navigation Systems (INS) are a good example of devices which are independent of external perturbations. Particularly, an inertial estimation unit (IMU) is an electronic gadget that measures and reports a body's specific force, angular rate, and orientation, utilizing a blend of accelerometers, gyroscopes, and sometimes magnetometers. IMUs are normally used in airplanes, including unmanned aeronautical vehicles (UAVs), and spacecraft. However, these systems also feature important lacks, such as frequent incorrect initialization, accelerometer and gyroscope imperfections, which are trigger for cumulative errorsand imperfections in implemented dynamics model. Despite of this fact, Inertial Navigation Systems, when hybridized with GNSS receivers to minimize drift, are excellent for GNC data acquisition [1; 2]. However, precision and cost are counterposed objectives. Reducing costs, while maintaining safety and accuracy standards, is key for development in aerospace engineering. Advanced algorithms for getting such standards while cutting down costs are are cornerstone. For example, to maintain an acceptable precision level while reducing costs, less precise devices may substitute expensive systems as long as GNSS signal is reachable and persistent to update the inertial system. However, many scenarios feature high uncertainty and alternatives are needed. An option to satisfy accuracy needs and budget limitations is to merge data of a few low cost sensors, which makes possible increases in accuracy levels. The advantages of coordinated combination of information have appeared in numerous air applications [3]. For example, information combination strategies for six degrees of freedom rockets are depicted in [4]. The main issues in using various sorts of INS augmented with GNSS updates have been considered by [5]. Notwithstanding INS/GNSS hybridization, a set of nonlinear observers are presented by [6]. Note that, in case there are various sensors available, they may be additional contributions to a filter, e.g., the Kalman filter [1; 2]. As it is shown in [1; 2], the need to develop new Guidance, Navigation and Control (GNC) frameworks has fostered research on stability and controllability of aerospace vehicles. A novel guidance law is presented in [7], where only observations of line-of-sight angle and its rate of change coming from a seeker are employed. Ref. [8; 9] present GNC cooperative techniques based on the conventional Proportional Navigation (PN). In [10] a target-follower engagement is considered, in which the target is followed while it tries to prevent interception. An attitude control-framework device for a spinning sounding rocket, which depends on a proportional, integral, and derivative (PID) controller, is created in [11]. Proportional-derivative GNC laws for the terminal phases of flight are proposed in [12; 13]. In [14], a limited time concurrent sliding-mode GNC law is introduced. An overall scheme concerning the guidance and autopilot modules for a class of spin-stabilized balance controlled devices is introduced in [15]. Yet, even in GNSS/IMU hybrid devices, there exist negative influences, such as irregular estimations, which might be predominant during terminal guidance. Other methods, which are based on image recognition using multispectral cameras and other sensors, may be used in navigation for aerospace applications [16]. However, they usually feature high costs.