Precisely predicting the location of the user in a Global-Navigation-Satellite-System-degraded environment is a highly challenging task. Localization based on cellular signal fingerprints is one of the promising solutions to this problem and has attracted increasing attention. Long Term Evolution (LTE) signal is popularly utilized for localization due to its global usage, extensive urban coverage, and favorable signal properties. This paper proposes a new multiband multicell Reference Signal Received Power (MBMC-R) fingerprint, which properly fuses LTE signals’ carrier band information, the physical cell identifier information, and RSRP values. Next, a sequential block-matching weight K nearest neighbor algorithm with a cosine similarity criterion is specially designed for performing the pattern-matching localization with the MBMC-R fingerprint. The proposed method also includes the derivation of the Cramer-Rao lower bound, which reveals the impact of various factors on the lower bound of position error. Simulation and on-field experiments prove the performance superiority over other fingerprint localization algorithms reported in the literature.

中文翻译：

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多频段和多小区的蜂窝信号 RSRP 指纹定位


在全球导航卫星系统退化的环境中精确预测用户的位置是一项极具挑战性的任务。基于蜂窝信号指纹的定位是解决该问题的有前途的解决方案之一，并引起了越来越多的关注。长期演进 (LTE) 信号因其全球使用、广泛的城市覆盖范围和良好的信号特性而被广泛用于定位。本文提出了一种新的多频段多小区参考信号接收功率（MBMC-R）指纹，它正确融合了 LTE 信号的载波频段信息、物理小区标识符信息和 RSRP 值。接下来，专门设计了具有余弦相似性准则的顺序块匹配权重K最近邻算法，用于使用MBMC-R指纹执行模式匹配定位。该方法还包括 Cramer-Rao 下界的推导，揭示了各种因素对位置误差下界的影响。仿真和现场实验证明其性能优于文献报道的其他指纹定位算法。