基于稀疏栅格优化的蜂窝车联网定位算法

蜂窝车联网（cellular-V2X，C-V2X）中的定位方案是车路协同与车联网业务发展的重要技术途径之一。目前基于基站、卫星等诸多定位方案，在车联网业务以及车路协同场景中常会遇到定位精度、定位处理时延、部署成本等诸多方面的挑战。针对这些问题，文章对已有栅格定位算法进行优化，提出一种基于统计信息网格（statistical information grid，STING）的稀疏栅格优化算法和基于极端梯度提升（extreme gradient boosting decision tree，XGBoost）进行指纹定位的车联网指纹定位算法。从栅格优化的角度出发，相较于传统指纹定位方法在定位精度和计算速率方面进行了优化，使其更适应于车路协同场景。该算法为目前的车联网定位提供了一种有效的定位方法。

New Location Algorithm Based on Sparse Grid Optimization in C-V2X

The location algorithm in cellular-V2X (C-V2X) has always been one of the important technical approaches for the development of vehicle-road collaboration and autonomous driving. Currently, in the vehicle-road collaboration scenarios of autonomous driving services, many positioning solutions including base stations and GNSS meet challenges in many aspects such as positioning accuracy, positioning processing delay and deployment cost. In response to these problems, a fingerprint location algorithm is proposed for C-V2X based on statistical information grid (STING) algorithm for grid optimization and extreme gradient boosting decision tree (XGBoost). Compared with traditional fingerprint positioning methods, the positioning accuracy and calculation rate are optimized after grid optimization. The new method is more suitable for vehicle-road collaboration scenarios, and provides an effective positioning method for C-V2X scenarios.