论文标题
LOS/NLOS混合环境中的基于地理空间 - 周期性信息的3D合作定位
Geo-Spatio-Temporal Information Based 3D Cooperative Positioning in LOS/NLOS Mixed Environments
论文作者
论文摘要
我们为无线网络提供了基于地理和时空信息的分布式合作定位（GSTICP）算法，该算法需要三维（3D）坐标并在视线（LOS）和非线（NLOS）混合环境中运行。首先，通过将位置向量估计的A后验分布分配并映射节点的空间域和时间域操作来创建因子图（FG）。然后，我们利用基于地理信息的NLOS识别方案来减少NLOS测量引起的性能降解。此外，我们利用了有限的基于对称的对称采样的缩放量表（SUT）方法来近似于fg上传递的消息的非线性项，尽管仅使用了少量样品。最后，我们提出了增强的锚升级（EAU）机制，以避免冗余迭代。我们的GSTICP算法支持可以确定节点之间距离的任何类型的范围测量。仿真结果和分析表明，我们的GSTICP的计算复杂性低于基于最新的信念传播（BP）的本地化，同时实现了更具竞争力的定位性能。
We propose a geographic and spatio-temporal information based distributed cooperative positioning (GSTICP) algorithm for wireless networks that require three-dimensional (3D) coordinates and operate in the line-of-sight (LOS) and nonline-of-sight (NLOS) mixed environments. First, a factor graph (FG) is created by factorizing the a posteriori distribution of the position-vector estimates and mapping the spatial-domain and temporal-domain operations of nodes onto the FG. Then, we exploit a geographic information based NLOS identification scheme to reduce the performance degradation caused by NLOS measurements. Furthermore, we utilize a finite symmetric sampling based scaled unscented transform (SUT) method to approximate the nonlinear terms of the messages passing on the FG with high precision, despite using only a small number of samples. Finally, we propose an enhanced anchor upgrading (EAU) mechanism to avoid redundant iterations. Our GSTICP algorithm supports any type of ranging measurement that can determine the distance between nodes. Simulation results and analysis demonstrate that our GSTICP has a lower computational complexity than the state-of-the-art belief propagation (BP) based localizers, while achieving an even more competitive positioning performance.
