RF source-seeking by a micro aerial vehicle using rotation-based angle of arrival estimates

We investigate a computationally and memory efficient algorithm for radio frequency (RF) source-seeking with a single-wing rotating micro aerial vehicle (MAV) equipped with a directional antenna. The MAV is assumed to have no knowledge of its position and to have only an estimate of orientation through a magnetometer. A key novelty of our approach is in exploiting the rotation of the MAV and the directionality of its RF antenna to derive estimates of the angle of arrival (AOA) at each rotation. The MAV then follows the estimated direction until the next rotation is complete. We prove convergence of this greedy algorithm under rather weak assumptions on the noise associated with the AOA estimates, using recent results on the property of recurrence for systems governed by stochastic difference inclusions. These convergence results are supplemented by simulations quantifying the amount of excess travel, relative to the straight line distance to the source. Indoor experiments using Lockheed Martin's Samarai MAV demonstrate the efficacy of the greedy algorithm both for static source-seeking, and for the more challenging problem of tracking a moving source.