Low-Cost Geolocation for Laser Target Designators in Unmanned Aerial Systems

Military Unmanned Aerial Systems (UAS) utilize high-cost Laser Target Designators (LTD) for intelligence and targeting operations. This research attempts to fill the LTD access gap by producing a low-cost and opensourced LTD solution for under $500. Using a multi-rotor drone, a GPS, and a LiDAR range finder, this research will use flight data from these sensors to produce geolocation results (latitude and longitude) of where the LiDAR sensor is impacting the target object. The key variables derived from the flight controller are altitude, yaw, roll, and pitch. The LiDAR sensor produces distance from the UAS to the target object. Using these variables, an open-source python code is built to determine the location of the object classified. Virtual offsets of the GPS, flight controller, and LiDAR sensor are incorporated into the code to allow for increased geolocation precision. Using Pythagorean theorem, we are able to solve for ground distance from the UAS's location to the target object. After ground distance is solved, yaw (direction) can be incorporated to determine the target object’s location. The results demonstrate a 95% efficiency in solving the object’s location using automated geolocation compared to manual calculations. Further, automated geolocation allows for real-time transcribing into a dataset which can further be plotted in a three dimensional plane to facilitate understanding of the locations of objects of interest. We consider this an acceptable amount of efficiency to allow real time transcribing in a variety of scenarios, mainly for initial experimental code that can be further improved upon.