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A Reliable Hybrid Positioning Methodology for Land Vehicles Using Low-Cost Sensors

机译：使用低成本传感器的陆地车辆可靠混合定位方法

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摘要

In this paper, we propose a reliable hybrid positioning methodology by combining the advantages of filter and extreme learning machine (ELM), which addresses GPS outages and uncertain nonlinear drift of MEMS INS simultaneously. A novel parallel-dual- filtering (PDHF) mechanism is proposed to prevent the filter from diverging during GPS outages and to make full use of supplementary observations. The PDHF is composed of an enhanced filter and an auxiliary filter. The enhanced filter is developed by fusing not only GPS information but also supplementary observations, which include yaw angle provided by electronic compass, longitudinal velocity derived from wheel speed sensor, and lateral velocity constrained by assumptions, whereas the auxiliary filter only fuses the supplementary observations. Furthermore, an ELM module with good generalization ability is designed and augmented with the auxiliary filter to constitute a “virtual enhanced filter.” In the case of GPS outages, the “virtual enhanced filter” provides accurate corrections for stand-alone INS. Due to the characteristic- of the filter, the proposed methodology is immune to uncertain nonlinear drift of MEMS INS in nature. To verify the effectiveness of the proposed methodology, road-test experiments with various scenarios were performed. The experimental results indicate that the proposed methodology outperform all the compared counterparts.
机译：在本文中，我们结合滤波器和极限学习机（ELM）的优点，提出了一种可靠的混合定位方法，该方法同时解决了GPS中断和MEMS INS不确定的非线性漂移问题。提出了一种新颖的并行双重滤波（PDHF）机制，以防止滤波器在GPS中断期间发散，并充分利用补充性观测资料。 PDHF由增强型滤波器和辅助滤波器组成。增强型滤波器的开发不仅融合了GPS信息，还融合了附加观测值，这些附加观测值包括电子罗盘提供的偏航角，来自车轮速度传感器的纵向速度以及受假设约束的横向速度，而辅助滤波器仅融合了附加观测值。此外，设计了具有良好泛化能力的ELM模块，并通过辅助过滤器进行了扩充，以构成“虚拟增强过滤器”。在GPS中断的情况下，“虚拟增强型过滤器”可为独立INS提供准确的校正。由于滤波器的特性，所提出的方法不受MEMS INS本质上不确定的非线性漂移的影响。为了验证所提出方法的有效性，在各种情况下进行了路测实验。实验结果表明，所提出的方法优于所有比较的同类方法。

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