arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2409.03433eess.SYcs.SY
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一种面向城市区域紧耦合GNSS/INS/视觉导航的基于新息的周跳、多径估计、检测与抑制方法

An innovation-based cycle-slip, multipath estimation, detection and mitigation method for tightly coupled GNSS/INS/Vision navigation in urban areas

Bo Xu, Shoujian Zhang, Jingrong Wang, Jiancheng Li

AI总结:

本文针对城市环境中GNSS信号易受遮挡导致周跳和多径的问题,提出基于新息的估计、检测与抑制方法I-EDM,并在GNSS/INS/视觉紧耦合系统中验证其定位精度较残差方法最大提升21.6%。

摘要:

精确、一致且可靠的定位对于众多应用至关重要。为了实现高精度全球定位服务,诸如全球导航卫星系统(GNSS)/惯性导航系统(INS)/视觉组合系统等多传感器融合技术综合了各传感器的优势。该技术对于复杂环境中的定位不可或缺,并已在大众市场得到广泛应用。然而,城市环境中频繁的信号衰减和遮挡加剧了GNSS定位性能的退化,并对多传感器组合系统的性能产生不利影响。针对城市环境中的GNSS伪距和载波相位观测数据,我们提出了一种基于新息的周跳/多径估计、检测与抑制(I-EDM)方法,以降低城市环境中由遮挡引起的多径效应和周跳对定位的影响。该方法利用聚类分析方法获取GNSS观测量的新息。随后,利用这些新息检测周跳和多径。与基于残差的方法相比,基于新息的方法避免了最小二乘法引起的残差过拟合,从而能够更好地检测GNSS观测量中的异常值。在城市环境中进行的车载测试验证了所提方法。实验结果表明,采用I-EDM方法的GNSS/INS/视觉紧耦合系统在东向、北向和天向分量上分别可以达到0.23米、0.11米和0.31米的精度,与基于残差的EDM(R-EDM)方法相比,最大提升了21.6%。

英文摘要:

Precise, consistent, and reliable positioning is crucial for a multitude of uses. In order to achieve high precision global positioning services, multi-sensor fusion techniques, such as the Global Navigation Satellite System (GNSS)/Inertial Navigation System (INS)/Vision integration system, combine the strengths of various sensors. This technique is essential for localization in complex environments and has been widely used in the mass market. However, frequent signal deterioration and blocking in urban environments exacerbates the degradation of GNSS positioning and negatively impacts the performance of the multi-sensor integration system. For GNSS pseudorange and carrier phase observation data in the urban environment, we offer an innovation-based cycle slip/multipath estimation, detection, and mitigation (I-EDM) method to reduce the influence of multipath effects and cycle slips on location induced by obstruction in urban settings. The method obtains the innovations of GNSS observations with the cluster analysis method. Then the innovations are used to detect the cycle slips and multipath. Compared with the residual-based method, the innovation-based method avoids the residual overfitting caused by the least square method, resulting in better detection of outliers within the GNSS observations. The vehicle tests carried out in urban settings verify the proposed approach. Experimental results indicate that the accuracy of 0.23m, 0.11m, and 0.31m in the east, north and up components can be achieved by the GNSS/INS/Vision tightly coupled system with the I-EDM method, which has a maximum of 21.6% improvement when compared with the residual-based EDM (R-EDM) method.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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