Autonomous Predictive Target Tracking of UAS with a Two-Axis Gimbal System

Abstract

The Unmanned Aircraft Systems Engineering (UASE) team at the University of North Dakota has been conducting research on autonomous target tracking for UAS operations for the last few years. A predictive algorithm was developed to improve and assist in the pointing knowledge and tracking accuracy of a ground based two-axis gimbal system. By utilizing the global positioning system (GPS) data provided by the aircrafts autopilot system, a closed form analytical expression of the gimbal position angles was developed. Before predictive pointing, a method that used the aircrafts last updated location to develop a pointing vector was utilized. This methodology proved to lack in pointing accuracy due to the lack of knowledge of the aircrafts real-time location. The process flow involves multiple subsystems that are used to develop the required rotational angles and velocities of the gimbal system. Therefore, by the time this data is calculated the aircraft will have moved to a different location resulting in the gimbal pointing to an incorrect location lagging the actual target. To accurately track the target the predictive algorithm uses a combination of position and velocity control algorithms to calculate the current and future locations of the aircraft. The position control is used for the initial tracking of the aircraft, while the velocity control allows the gimbal to continually move to each new location without starting and stopping between inputs. In this manner the gimbal is able to “keep up” with the target since the gimbal is in continual motion, as is the aircraft. By storing the aircrafts past known locations in an array, a predicted location of the aircraft is able to be calculated using a various number of past locations to best fit the trajectory. This algorithm has been tested in simulation, mobile ground testing, and flight testing. Initial flight tests were conducted in North Dakota in early July 2010. For the flight tests a camera was attached to the gimbal as it tracked the aircraft. The system showed that the tracking of the aircraft was more accurate than the original non-predictive tracking algorithm.