TY - GEN
T1 - UAV team formation for emitter geolocation
AU - Marsh, Luke
AU - Gossink, Don
AU - Drake, Samuel Picton
AU - Calbert, Greg
PY - 2007/2
Y1 - 2007/2
N2 - In this paper we study a scenario in which uninhabited aerial vehicles (UAVs) are tasked with locating a group of active emitters. The Time Difference of Arrival (TDOA) technique is used by the UAVs to geolocate the active emitters. As TDOA requires at least three UAVs to perform geolocation, an algorithm to team three or more UAVs and task this team to geolocate an emitter is required. We discuss two approaches for teaming the UAVs, which we have developed and tested via simulation. The first approach is a simple heuristic that assigns the closest three UAVs to the highest priority emitter. This approach was simple and efficient to run, but improvements in performance could be made. Because TDOA works best when the UAVs are angularly separated evenly around the emitter, the next teaming algorithm developed takes into account the geometry between all UAVs and emitters. It assigns a cost for each UAV team and emitter combination based on the current angular separation of the UAVs around an emitter, and by how much each UAV team can perfect its geometry in a given time period. Two search techniques are explored, which search the solution space for efficient solutions. Results from simulation tests of the two approaches indicate that the second algorithm on average geolocates the emitters in the simulation faster than the heuristic approach, and provides an efficient solution to this UAV teaming and allocation problem.
AB - In this paper we study a scenario in which uninhabited aerial vehicles (UAVs) are tasked with locating a group of active emitters. The Time Difference of Arrival (TDOA) technique is used by the UAVs to geolocate the active emitters. As TDOA requires at least three UAVs to perform geolocation, an algorithm to team three or more UAVs and task this team to geolocate an emitter is required. We discuss two approaches for teaming the UAVs, which we have developed and tested via simulation. The first approach is a simple heuristic that assigns the closest three UAVs to the highest priority emitter. This approach was simple and efficient to run, but improvements in performance could be made. Because TDOA works best when the UAVs are angularly separated evenly around the emitter, the next teaming algorithm developed takes into account the geometry between all UAVs and emitters. It assigns a cost for each UAV team and emitter combination based on the current angular separation of the UAVs around an emitter, and by how much each UAV team can perfect its geometry in a given time period. Two search techniques are explored, which search the solution space for efficient solutions. Results from simulation tests of the two approaches indicate that the second algorithm on average geolocates the emitters in the simulation faster than the heuristic approach, and provides an efficient solution to this UAV teaming and allocation problem.
UR - http://www.scopus.com/inward/record.url?scp=34548813355&partnerID=8YFLogxK
U2 - 10.1109/IDC.2007.374545
DO - 10.1109/IDC.2007.374545
M3 - Conference contribution
AN - SCOPUS:34548813355
SN - 9781424409020
T3 - Conference Proceedings of 2007 Information, Decision and Control, IDC
SP - 176
EP - 181
BT - Conference Proceedings of 2007 Information, Decision and Control, IDC
PB - Institute of Electrical and Electronics Engineers
CY - South Australia
T2 - 2007 Information, Decision and Control, IDC
Y2 - 12 February 2007 through 14 February 2007
ER -