  In this paper, we investigate the problem of UAV-aided user localization in
wireless networks. Unlike the existing works, we do not assume perfect
knowledge of the UAV location, hence we not only need to localize the users but
also to track the UAV location. To do so, we utilize the time-of-arrival along
with received signal strength radio measurements collected from users using a
UAV. A simultaneous localization and mapping (SLAM) framework building on the
Expectation-Maximization-based least-squares method is proposed to classify
measurements into line-of-sight or non-line-of-sight categories and learn the
radio channel, and at the same, localize the users and track the UAV. This
framework also allows us to exploit other types of measurements such as the
rough estimate of the UAV location available from GPS, and the UAV velocity
measured by an inertial measurement unit (IMU) on-board, to achieve better
localization accuracy. Moreover, the trajectory of the UAV is optimized which
brings considerable improvement to the localization performance. The
simulations show the out-performance of the developed algorithm when compared
to other approaches.
