During aircraft pilot training, flight briefings play a critical role in reinforcing key concepts, reviewing safety procedures, and addressing any operational or regulatory considerations. However, post-flight briefings require validated information about the entire route taken by the aircraft, and not all airplanes have the devices and tools to support these briefings. In fact, flight mission monitoring and analysis systems are often proprietary, and the costs of using them can be quite high. Therefore, this work proposes the design and implementation of the necessary hardware and software for the tracking and analysis of flight missions carried out by light airplanes. The implemented prototype includes a small electronic device with an external GPS (Global Positioning System) antenna that records the raw geographic coordinates of the route followed by the aircraft. Those raw coordinates are entered into a computer program that allows for the visualization and analysis of various flight parameters, such as position, velocity, and acceleration. Due to errors produced by commercial GPS devices, an extended Kalman filter and a machine learning-based regression have been used to generate smoothed routes that consider the inertia of moving objects, increasing positioning accuracy. Unmanned aerial vehicles were used to evaluate this prototype, besides some other tests with ground vehicles. The results are quite encouraging given the budget constraints, obtaining average accuracies of less than 6.4 m for spatial positioning, 2.3 m/s for velocity estimation, and 0.5 m/s \(^2\) for acceleration.