Unscented Kalman Filtering for Real-Time Atmospheric Thermal Tracking

Abstract

The increasing use of unmanned air vehicles in military and civilian applications has been accompanied by a growing demand for improved endurance and range. These demands have been largely met by advances in aerodynamic and structural efficiency, improved battery technology, and the ongoing miniaturization of onboard computing and payload systems. Recently, more attention has been paid to the extraction of energy from the atmosphere. Aircraft can make use of atmospheric updrafts, or thermals, to gain altitude without expenditure of onboard fuel stores. By intelligently tracking thermals, an unmanned aircraft can extend its range or loiter time without carrying additional fuel or specialized sensors.

Prior research has focused on the `big picture' concepts associated with autonomous soaring - determining when to stop and soar in a thermal, what speed to fly, when to return to the desired course, and so on. Finding and tracking thermals is only a single component of the complete soaring system. However, because the high-level decision making tasks rely on estimates of the thermal parameters, the accuracy and computational cost of the thermal tracking algorithm set the upper performance limit of the entire system.

So, this research reformulated batch regression thermal finding algorithms used in past efforts into an efficient Unscented Kalman Filter. Open-loop simulation results showed the filter was capable of accurately estimating thermal position, strength, and size with low computational cost for a variety of realistic flight paths. Closed-loop simulation reaffirmed this statement in the presence of realistic aircraft, sensor, and thermal dynamics. Further, the algorithm was embedded into the ALOFT soaring platform (a 4.3m wingspan unmanned glider) for flight testing, which demonstrated its ability to track real-world thermals during cross-country flights exceeding 5 hours flight time over a 70 mile course.