The development of unmanned vehicles has presented serious privacy and security threats, especially to the military, in the last decade. The military's capability to fight against drones is imperative as the dynamic of warfare changes. For drone detection, classification determines whether the drone is friendly or hostile. The next step is to find the estimation of the pose or the range and relative attitude. Utilizing both steps allows the user to track aimpoints of the drone for targeting. This research will evaluate a possible solution, convolutional neural networks, for creating a precise pose estimation system for drone surveillance.