With the broad deployment of Wi-Fi networks, the Received Signal Strength (RSS) based Wi-Fi indoor localization has attained much interest of both academia and industry. At present, most of the currently available Wi-Fi indoor localization techniques focus on increasing the localization accuracy. However, few of them take into account the diversity of Wi-Fi signal distributions and the measurement error associated with RSS values owing to the complicated indoor environment, which consequently results in the low robustness of indoor localization systems. Thus, with the motivation to tackle this gripping problem, we design a new hybrid hypothesis test based on the idea of Asymptotic Relative Efficiency (ARE), which exploits signal distributions by considering different Access Point (AP) contributions to the Wi-Fi indoor localization accuracy. In concrete terms, first of all, the Jarque-Bera (JB) test is used to perform the normality test on the Wi-Fi signal distribution at each Reference Point (RP), and then the Chi-squared Automatic Interaction Detection (CHAID) approach is applied to obtain each AP contribution degree. Secondly, based on the evaluation of the JB test on the Wi-Fi signal distribution, the hybrid Mann-Whitney U and T test is applied to find the set of matching RPs corresponding to each newly-collected RSS data. Finally, the target location estimate is acquired by using the K-Nearest Neighbor (KNN), where the contribution degree of each AP is assigned as the weight during the calculation to find matching RPs. From the extensive experimental results, it is evident that the proposed approach can successfully improve the system performance by achieving a higher localization accuracy and enhanced robustness when compared with the state-of-the-art Wi-Fi indoor localization techniques.

中文翻译：

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Wi-Fi 室内定位信号分布和接入点贡献的综合统计测试


随着Wi-Fi网络的广泛部署，基于接收信号强度（RSS）的Wi-Fi室内定位引起了学术界和工业界的广泛关注。目前，现有的Wi-Fi室内定位技术大多侧重于提高定位精度。然而，由于室内环境复杂，很少考虑Wi-Fi信号分布的多样性以及与RSS值相关的测量误差，从而导致室内定位系统的鲁棒性较低。因此，为了解决这个棘手的问题，我们设计了一种基于渐近相对效率 (ARE) 思想的新混合假设检验，该检验通过考虑不同接入点 (AP) 对 Wi-Fi 室内定位的贡献来利用信号分布准确性。具体来说，首先使用Jarque-Bera（JB）检验对每个参考点（RP）处的Wi-Fi信号分布进行正态性检验，然后进行卡方自动交互检测（CHAID）方法用于获得每个AP贡献度。其次，基于JB测试对Wi-Fi信号分布的评估，应用混合Mann-Whitney U和T测试来找到与每个新收集的RSS数据对应的匹配RP集合。最后，利用K近邻（KNN）获得目标位置估计，在计算过程中将每个AP的贡献度指定为权重，以找到匹配的RP。 从大量的实验结果来看，与最先进的 Wi-Fi 室内定位技术相比，所提出的方法可以通过实现更高的定位精度和增强的鲁棒性来成功提高系统性能。