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

Abstract

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.