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IEEE 802.11 Wi-Fi access point localization with angular signal strength information

机译:具有角度信号强度信息的IEEE 802.11 Wi-Fi接入点定位

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A better understanding of the spatial structure of existing opportunistic wireless network deployments will offer new insights for future deployments, e.g. dense femtocell networks. In this paper we present a performance analysis for three different localization algorithms that support such efforts by finding the location of IEEE 802.11 Wi-Fi access points (APs) from distributed signal strength measurements. These algorithms extend prior works on omnidirectional single-point measurements and includes angular information into the estimation process. Using a custom-made measurement setup, we have collected data for typical indoor and outdoor scenarios which are likely to be encountered in large measurement campaigns. We quantify and reason on the localization error for each scenario and algorithm, and propose a weight spread estimate for determining the robustness of the localization approaches. Through variations in the used sample data set we further discuss relevant observations on the stability of the different algorithmic estimation approaches. Our results show that overall propagation-centric algorithms perform best, but that algorithms that build a stochastic model of angular signal strength distribution are more robust if only few measurements are available.
机译:对现有机会性无线网络部署的空间结构的更好理解将为未来的部署提供新的见解,例如:密集的毫微微小区网络。在本文中,我们针对三种不同的本地化算法进行了性能分析,这些算法通过从分布式信号强度测量中找到IEEE 802.11 Wi-Fi接入点(AP)的位置来支持此类工作。这些算法扩展了对全向单点测量的现有工作,并将角度信息纳入了估计过程。使用定制的测量设置,我们收集了在大型测量活动中可能遇到的典型室内和室外场景的数据。我们对每种情况和算法的定位误差进行量化和推理,并提出权重扩展估计值以确定定位方法的鲁棒性。通过使用样本数据集的变化,我们进一步讨论了有关不同算法估计方法稳定性的相关观察。我们的结果表明,总体上以传播为中心的算法性能最佳,但是如果只有很少的测量可用,则构建角度信号强度分布的随机模型的算法将更加健壮。

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