Unmanned aerial vehicles (UAVs) play a crucial role in various applications, including agriculture, disaster management, and military purposes, where location integrity and awareness are essential. However, current methods relying on GPS signals face challenges in extreme conditions, such as signal unavailability or compromise. To address this limitation, this project investigates a trajectory-based analysis, inspired by the nautical practice of "dead reckoning," which estimates the position of a moving object based on its movement information, including previous position, velocity, direction, and elapsed time. This approach is modeled as a time-series problem, utilizing deep sequence models to learn temporal patterns and interdependencies, offering a complementary solution to existing GPS-based methods that can work effectively in challenging circumstances, such as limited GPS availability, software malfunctions, and emerging attacks, thereby enhancing the security and reliability of UAV operations. This project contributes to empowering UAV-assisted designs in various research sectors, such as cellular-connected UAVs, the Internet of Drones, and Flying Ad-hoc Networks (FANETs). Additionally, this project supports students in gaining AI/ML skills and workforce development through reinforced educational programs and research activities. This project aims to develop a deep analytics framework that offers core functionality for location integrity and awareness based on traje