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一种利用超宽带（UWB）和车载传感器在十字路口进行车辆定位的融合策略。

A Fusion Strategy for Vehicle Positioning at Intersections Utilizing UWB and Onboard Sensors.

作者信息

Gao Huaikun, Li Xu, Song Xiang

机构信息

School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China.

School of Electronic Engineering, Nanjing Xiaozhuang University, Nanjing 211171, China.

出版信息

Sensors (Basel). 2024 Jan 12;24(2):0. doi: 10.3390/s24020476.

DOI:10.3390/s24020476
PMID:38257571
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11154252/
Abstract

For vehicle positioning applications in Intelligent Transportation Systems (ITS), lane-level or even more precise localization is desired in some typical urban scenarios. With the rapid development of wireless positioning technologies, ultrawide bandwidth (UWB) has stood out and become a prominent approach for high-precision positioning. However, in traffic scenarios, the UWB-based positioning method may deteriorate because of not-line-of-sight (NLOS) propagation, multipath effect and other external interference. To overcome these problems, in this paper, a fusion strategy utilizing UWB and onboard sensors is developed to achieve reliable and precise vehicle positioning. It is a two-step approach, which includes the preprocessing of UWB raw measurements and the global estimation of vehicle position. Firstly, an ARIMA-GARCH model to address the NLOS problem of UWB at vehicular traffic scenarios is developed, and then the NLOS of UWB can be detected and corrected efficiently. Further, an adaptive IMM algorithm is developed to realize global fusion. Compared with traditional IMM, the proposed AIMM is capable of adjusting the model probabilities to make them better matching for current driving conditions, then positioning accuracy can be improved. Finally, the method is validated through experiments. Field test results verify the effectiveness and feasibility of the proposed strategy.

摘要

对于智能交通系统（ITS）中的车辆定位应用，在一些典型的城市场景中需要车道级甚至更精确的定位。随着无线定位技术的快速发展，超宽带（UWB）脱颖而出，成为高精度定位的一种重要方法。然而，在交通场景中，基于UWB的定位方法可能会由于非视距（NLOS）传播、多径效应和其他外部干扰而变差。为了克服这些问题，本文提出了一种利用UWB和车载传感器的融合策略，以实现可靠且精确的车辆定位。这是一种两步法，包括UWB原始测量值的预处理和车辆位置的全局估计。首先，开发了一种ARIMA - GARCH模型来解决车辆交通场景中UWB的NLOS问题，然后可以有效地检测和校正UWB的NLOS。此外，还开发了一种自适应交互式多模型（AIMM）算法来实现全局融合。与传统的交互式多模型（IMM）相比，所提出的AIMM能够调整模型概率，使其更好地匹配当前驾驶条件，从而提高定位精度。最后，通过实验对该方法进行了验证。现场测试结果验证了所提策略的有效性和可行性。

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