Small aircraft carriers play a crucial role in military navigation and field intelligence. Unmanned Combat Aerial Vehicles (UCAVs) have emerged as indispensable tools for various military operations. However, the efficacy of UCAV operations is often hampered by the turbulent conditions generated by oceanic gusts. To enhance the reliability and autonomy of these systems, this research focuses on advancing aerodynamic load estimation techniques.
 Many biological systems employ sensory systems to collect environmental feedback to improve predation and avoid attack. Examples include the proprioception exhibited in bird feathers and the lateral line function in fish. Drawing inspiration from these biological models, a system of sensors is employed to achieve more precise estimations of the UCAV aerodynamic state and therefore train an existing Multi-Layer Perception Artificial Neural Network (MLP ANN) to respond to the environment.
 A series of experiments were conducted on a non-slender delta-wing model of NACA 0012 mounted on a force/moment sensor and exposed to varying flow conditions in a controlled setup called the WindShaper. To simulate gusty environments, the WindShaper produced unsteady random axial gusts ranging from 5m/s to 15m/s. A total of 100 sets of data, consisting of 60000 data points were collected and used to train the MLP ANN.
 Statistical methods were employed to distinguish valid and edge cases within the datasets. From analyzing the outputs generated by the MLP ANN, it was determined that there exists an importance in collecting data of high quality. These analyses identify factors affecting model performance and highlight specific challenges that hinder accurate gust prediction. The utilization of advanced neural network methodologies as a technique for load estimation has significant implications for naval aviation, aerodynamics, and their related autonomous systems. This research contributes to the development of more robust UCAV operations in challenging environments.