Localizing RF targets with cooperative unmanned aerial vehicles

Unmanned aerial vehicles (UAVs) play an important and expanding role in both civilian and military missions, such as search and rescue, intelligence collection, surveillance, or reconnaissance. Currently, UAVs require human operators to control and direct their flights and sensors. To expand their effectiveness and exploit their inherent capabilities, we seek to develop robust techniques for multiple UAVs to cooperatively operate without direct human control. Our current research interest is to develop algorithms and simulate techniques to enable UAVs to search for, detect, and locate mobile ground targets emitting radio frequency signals. This paper investigates the task of combining sensor data from multiple UAVs to obtain accurate and reliable target locations. The sensors collect only coarse angle-of-arrival information and we apply Kalman filtering techniques to estimate the angle to the target. The estimated angles from multiple UAVs are sufficient to develop control laws for the UAVs to converge on an orbit about the target and collect additional measurements to further improve the estimation of the target's position. We explore a sensor fusion process embedded in a simple control law that allows multiple UAVs to cooperate in the target localization task and coordinate their motion using a leader-follower approach. We demonstrate the cooperative sensing techniques using simulation results.