城市场景智能手机GNSS/MEMS融合车载高精度定位

MEMS-Enhanced Smartphone GNSS High-Precision Positioning for Vehicular Navigation in Urban Conditions

  • 摘要: 智能手机凭借其普遍性、便携性和低成本等优势,已成为大众用户导航与位置服务的主流终端载体,其多频多系统GNSS(global navigation satellite system)观测值的开放进一步激发了手机高精度定位的研究。然而,受限于消费级GNSS器件性能,手机卫星观测值呈现出信号衰减严重、伪距噪声大、粗差周跳多等问题;并且受城市复杂环境影响,手机GNSS定位的连续性、可靠性也难以保证。提出一种城市场景手机GNSS/ MEMS(micro-electro mechanical system)融合的车载高精度定位方案。首先,构建了速度约束的GNSS差分定位模型;然后,通过手机内置MEMS与车辆运动约束,在挑战环境下进行GNSS/MEMS融合精密定位。实验结果表明,在开阔和树荫场景下,速度约束方法可达到分米至米级定位精度,相比于常规方法分别提升了35.2%和78.9%;在高架场景下,GNSS/MEMS融合定位的精度和连续性均提升显著;在隧道场景下,MEMS推算位置累积误差约为2.5%。实验结果初步表明,手机GNSS具备开阔环境下的车道级定位能力,手机GNSS/MEMS融合可提升城市复杂环境下车载定位的精度和连续可用性。

     

    Abstract:
      Objectives  Smartphones have become the mainstream terminal carrier of navigation and location services for mass users by virtue of their ubiquity, portability, and low cost. With the opening of their multi-frequency and multi-constellation global navigation satellite system (GNSS) observations, the research on high-precision positioning of smartphones has been further stimulated. However, limited by the performance of consumer-grade GNSS devices, the satellite observations of smartphones present problems such as serious signal attenuation, large pseudorange noise, and many cycle slips; and also affected by the complex urban environment, the continuity and reliability of smartphone GNSS positioning is also difficult to guarantee.
      Methods  A smartphone GNSS/MEMS (micro-electro mechanical system) integrated high-precision positioning scheme for vehicular navigation in urban conditions is proposed. Firstly, a velocity-constrained GNSS differential filtering positioning model is constructed to realize precise positioning in a general observation environment; then, through the built-in MEMS of smartphone and virtual constraints of vehicle motion, GNSS precise positioning is enhanced in challenging environments.
      Results  The experiment results show that, in open sky and tree occlusion conditions, the improved method can achieve decimeter-to-meter positioning accuracy, which is 35.2% and 78.9% higher than conventional method, respectively; in viaduct occlusion conditions, the accuracy and continuity of GNSS/MEMS fusion positioning are the best; in tunnel conditions, the cumulative position error of the MEMS mechanization is about 2.5%.
      Conclusions  The results preliminarily show that smartphone GNSS has lane-level positioning capabilities in open-sky environments, and GNSS/MEMS fusion can improve the accuracy and continuity of smartphone positioning in urban challenging environments.

     

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