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.

翻译：本文研究了无线网络中无人机辅助的用户定位问题。与现有研究不同，我们并未假设已知无人机位置的完美信息，因此不仅需要对用户进行定位，还需对无人机位置进行跟踪。为此，我们利用无人机收集用户处的到达时间与接收信号强度无线电测量数据。提出了一种基于期望最大化最小二乘法的同步定位与地图构建框架，将测量数据分类为视距或非视距类别，学习无线电信道，同时实现用户定位与无人机跟踪。该框架还能利用其他类型的测量数据（如GPS提供的无人机位置粗略估计、机载惯性测量单元测量的无人机速度）来提升定位精度。此外，通过优化无人机轨迹，定位性能得到显著提升。仿真结果表明，相较于其他方法，所提算法具有更优性能。