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WiFi Fingerprint Based Indoor Localization with Iterative Weighted KNN for WiFi AP Missing

机译：基于WiFi指纹的室内定位，用迭代加权KNN缺少WiFi AP

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摘要

In this paper, a real WiFi fingerprint-based indoor localization system is considered, where three primary components including the APP in smart phone, the server system and the embedded localization algorithm, have been designed. This paper proposes a dedicated data preprocessing algorithm to solve the singular-collection problem. Furthermore, the issue of WiFi access point (AP) missing is discussed and the theoretical analysis is presented under the condition of a two-AP scenario. Finally, because of unequal amount of location information contained in received signal strength (RSS) from different AP, the weighted RSS (WRSS) and the iterative weighted K nearest neighbor (IWKNN) algorithm are proposed for localization. Experimental results shows the proposed scheme achieves a competitive localization accuracy.
机译：在本文中，考虑了真实的WiFi指纹的室内定位系统，其中设计了包括智能手机，服务器系统和嵌入的本地化算法中的三个主要组件。本文提出了专用数据预处理算法来解决奇异收集问题。此外，讨论了WiFi接入点（AP）的问题，并且在双向方案的条件下呈现了理论分析。最后，由于来自不同AP的接收信号强度（RSS）中包含的不等的位置信息，提出了加权的RS（WRS）和迭代加权k最近邻（IWKNN）算法用于定位。实验结果表明，所提出的方案实现了竞争的本地化准确性。

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