# Applying Neural Networks in Aerial Vehicle Guidance to Simplify Navigation Systems
## 1 Introduction
Hence, advancement on new algorithms which may easily fulfill the required precision levels and budget limitations is a foundation in research. For instance, there are recent advances which consist of incorporating IMU, GPS, and laser guidance capacity, offering high accuracy and all-weather capacity [17; 18]. Laser guidance may be provided by means of Semi Active Laser Kits (SAL). These devices are applied in many designing areas, such as calculating rotational speed of objects and estimating dynamics of laser spots [19; 20]. The bonus of these kits is their favorable position during the last periods of the guidance, when they can provide high precision for GNC systems. Therefore, it can be stated that sensor hybridization techniques [16; 21] for viable and robust estimations are a current need when autonomy, accuracy, and minimal cost are to be achieved. However, also note that as the number of sensors to employed increases, the cost of system also increases. In this sense, Machine Learning (ML) techniques come onto the scene. They offer multitudinous options and innovative solutions of particular interest for GNC applications, where their foray is still latter and shallow, yet with no doubt promising. The utilization of ML strategies for the estimation of parameters dependent on the dynamics of aerial vehicles presents the bit of leeway that once the algorithm is calibrated or trained, it is not important to know the physical-mathematical establishments that rule the flight mechanics. Given the input signals, ML algorithms may restore the data that can later be utilized within the GNC system, such that the subsequent solutions will fit the genuine output [22; 23]. Taking benefit of these facts, a reduced set of sensors may be selected to work together with ML algorithms, all while safety and accuracy standards are matched, and complexity and costs are decreased. However, the application of these strategies to a wide set of scenarios, which may also include uncertain conditions, depends largely on the representativity and amount of input and output data employed for training ML algorithms. This fact implies that desired performance stability and convergence is to be restricted to the trained mission envelope. Other approaches, which could ensure convergence and stability parameters under the proposed uncertain conditions, might also be employed for this type of application. For instance, adaptive control that uses adaptation laws to online estimate unknown system parameter variations for various mission envelopes [24; 25; 26]. Altogether, the objective of this paper is to improve current guidance strategies applying a powerful hybridization approach, which also introduces ML to enable attitude determination with a reduced availability of sensors, namely GNSS, accelerometer and semiactive laser quadrant photo-detectors. In particular, neural networks (NN) are implemented to precisely estimate the gravity vector to be combined with velocity and line of sight vectors in order to determine the attitude or rotation of the vehicle without needing gyroscopes. Note that the mentioned vectors need to be obtained in two different reference frames because otherwise the attitude determination problem cannot be solved.