To improve the localization precision of unmanned aerial vehicle (UAV), a novel framework is established by jointly utilizing multiple measurements of received signal strength (RSS) from multiple base stations (BSs) and multiple points on trajectory. First, a joint maximum likelihood (ML) of exploiting both trajectory information and multi-BSs is proposed. To reduce its high complexity, two low-complexity localization methods are designed. The first method is from BS to trajectory (BST), called LCSL-BST. First, fixing the $n$ th BS, by exploiting multiple measurements along trajectory, the position of UAV is computed by ML rule. Finally, all computed positions of UAV for different BSs are combined to form the resulting position. The second method reverses the order, called LCSL-TBS. We also derive the Cramer-Rao lower boundary (CRLB) of the joint ML method. From simulation results, we can see that the proposed joint ML and separate LCSL-BST methods have made a significant improvement over conventional ML method without use of trajectory knowledge in terms of location performance. The former achieves the joint CRLB and the latter is of low-complexity.

中文翻译：

---

通过轨迹和多基站增强基于 RSS 的无人机定位

为了提高无人机 (UAV) 的定位精度，通过联合利用来自多个基站 (BS) 和轨迹上多个点的接收信号强度 (RSS) 的多次测量，建立了一种新颖的框架。首先，提出了利用轨迹信息和多基站的联合最大似然（ML）。为了降低其高复杂度，设计了两种低复杂度的定位方法。第一种方法是从BS到轨迹（BST），称为LCSL-BST。首先，固定 $n$ th BS，通过利用沿轨迹的多次测量，UAV 的位置由 ML 规则计算。最后，将 UAV 为不同 BS 计算的所有位置组合起来形成最终位置。第二种方法颠倒顺序，称为LCSL-TBS。我们还推导出了联合 ML 方法的 Cramer-Rao 下边界 (CRLB)。从仿真结果可以看出，所提出的联合 ML 和单独的 LCSL-BST 方法在定位性能方面比不使用轨迹知识的传统 ML 方法有了显着的改进。前者实现联合CRLB，后者复杂度低。