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A Probabilistic Radio Map Construction Scheme for Crowdsourcing-Based Fingerprinting Localization

机译：基于众包的指纹定位的概率无线电地图构建方案

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摘要

Fingerprinting localization using received signal strength (RSS) has been considered as a promising solution for indoor localization systems, due to its ease of use in deployed wireless networks. A major bottleneck is the difficult trade-off between localization accuracy and site survey costs. Crowdsourcing-based fingerprint collection is cost efficient because of user participation, but its accuracy is questioned due to the noisy sample locations. In this paper, we present a probabilistic radio map construction scheme with crowdsourcing collection, considering both accuracy and survey costs. In particular: 1) based on further studies of RSS properties, we introduce a concept of unfixed data collection—in which sample locations are estimated by an additional localization mechanism—and verify its effectiveness, although sample locations are noisy; 2) we present a modified parametric fitting method to better describe location signatures by transforming RSS into signal envelope; and 3) we propose a clustering-based space partitioning algorithm to improve the fitting effect by reducing the number of multimodal distributions. Extensive simulations and experiments show the proposed method can construct an accurate radio map at a low cost, and achieve a localization accuracy of about 2.5 m, 20% improvement compared to using the classical radio map in our experimental scenario.
机译：使用接收信号强度（RSS）进行指纹定位已经被认为是室内定位系统的有前途的解决方案，因为它易于在部署的无线网络中使用。一个主要的瓶颈是本地化准确性和站点调查成本之间的艰难权衡。基于众包的指纹收集由于用户参与而具有成本效益，但是由于样本位置嘈杂，其准确性受到质疑。在本文中，我们考虑了准确性和调查成本，提出了一种采用众包收集的概率无线电地图构建方案。特别是：1）在进一步研究RSS属性的基础上，我们引入了未固定数据收集的概念，其中样本位置通过附加的本地化机制进行估算，并验证其有效性，尽管样本位置嘈杂； 2）我们提出一种改进的参数拟合方法，通过将RSS转换为信号包络来更好地描述位置签名； 3）提出了一种基于聚类的空间划分算法，通过减少多峰分布的数量来提高拟合效果。大量的仿真和实验表明，所提出的方法可以以低成本构建准确的无线电地图，并实现约2.5 m的定位精度，与在我们的实验场景中使用经典无线电地图相比，提高了20％。

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