PROSPECTS OF APPLYING GAN NETWORKS FOR IMPROVING THE RELIABILITY OF DATA FROM UNMANNED AERIAL VEHICLE SENSORS

Abstract

This article investigates the application of generative adversarial networks (GANs) to enhance the precision and dependability of sensor data from unmanned aerial vehicles (UAVs), with a particular focus on data from inertial measurement units (IMUs) and Global Positioning Systems (GPS). The study demonstrates how GANs can be employed to increase the discretization of data and rectify anomalies within GPS data streams, thereby substantially improving the performance and accuracy of UAV navigation systems. It delves into the operational mechanics of GANs, exploring their capacity to generate high-quality, synthesized data that mimic authentic sensor readings closely. Additionally, the article examines the potential challenges and limitations associated with deploying GANs in this context, such as the need for extensive training datasets required for effective model training and deployment.Furthermore, the article discusses the implications of using GANs for real-time data correction and enhancement, highlighting the potential for these technologies to revolutionize UAV navigation by providing more reliable and accurate sensor data. By analyzing the benefits and addressing the complexities associated with integrating GANs into UAV systems, the article underscores the necessity for continued research and development in this area. The significance of further explorations into GAN applications for UAV navigation cannot be overstated. As the demand for UAVs continues to grow across various sectors, including agriculture, surveillance, and logistics, the need for advanced navigation systems that ensure operational safety and efficiency becomes increasingly critical. This article posits that GANs offer a promising solution to these challenges, paving the way for the development of next-generation UAV navigation systems that can operate with unprecedented accuracy and reliability.