Accurate wireless positioning of mobile agents is challenging in non-line-of-sight (NLOS) propagation environments due to unknown range or angle biases. In this paper, we develop a cooperative localization algorithm for mixed line-of-sight (LOS)/NLOS environments where the NLOS effect is mitigated by exploiting the geometric relationship of the range biases. In particular, we cast the localization problem as a detection-aided optimization program, in which all the distance measurements are initially treated as NLOS links with unknown nonnegative biases, followed by iterative agent position estimation and LOS identification. Moreover, the maximum-likelihood estimator for the agent positions and NLOS biases is relaxed into a semidefinite program where the geometric relationship of the biases is introduced as constraints. We also characterize the cooperation gain for LOS identification, and derive the constrained Cramér-Rao bound to show the localization accuracy improvement by the geometric constraints. Finally, numerical results validate the superior performance of the proposed algorithm compared with other competitive methods.

中文翻译：

---

通过合作定位中的空间几何开发减轻 NLOS 效应

由于未知的距离或角度偏差，移动代理的准确无线定位在非视距 (NLOS) 传播环境中具有挑战性。在本文中，我们开发了一种用于混合视线 (LOS)/NLOS 环境的协作定位算法，其中通过利用距离偏差的几何关系来减轻 NLOS 效应。特别是，我们将定位问题作为检测辅助优化程序，其中所有距离测量最初都被视为具有未知非负偏差的 NLOS 链接，然后是迭代代理位置估计和 LOS 识别。此外，代理位置和 NLOS 偏差的最大似然估计被放松为一个半定程序，其中偏差的几何关系被引入作为约束。我们还表征了 LOS 识别的合作增益，并推导出受约束的 Cramér-Rao 界，以显示几何约束对定位精度的提高。最后，数值结果验证了所提出算法与其他竞争方法相比的优越性能。