基于信号分布混合假设检验的Wi-Fi室内定位方法

Wi-Fi Indoor Localization Based on Hybrid Hypothesis Test of Signal Distribution

Wi-Fi室内定位技术是目前移动计算领域的研究热点之一,而传统位置指纹定位方法没有考虑复杂室内环境下Wi-Fi信号分布的多样性问题,从而导致Wi-Fi室内定位系统的鲁棒性较差。为了解决这一问题,该文提出一种基于信号分布混合假设检验的Wi-Fi室内定位方法。首先根据Jarque-Bera(JB)检验结果对各个参考点处的Wi-Fi信号分布进行正态性评价;然后针对不同Wi-Fi信号分布特性,利用混合Mann-Whitney U检验/T检验方法构造匹配参考点集合,以实现对目标的区域定位;最后通过计算定位区域中匹配参考点的K近邻(K-Nearest Neighbor, KNN),完成对目标的位置坐标估计。实验结果表明,所提方法相比于传统Wi-Fi室内定位方法具有更高的定位精度和更强的系统鲁棒性。

Wi-Fi indoor localization technique is one of the current research hotspots in the field of mobile computing, however, the conventional location fingerprinting based localization scheme does not consider the diversity of Wi-Fi signal distribution in the complicated indoor environment, resulting in the low robustness of indoor localization system. To address this problem, a new hybrid hypothesis test of signal distribution for Wi- Fi indoor localization is proposed. Specifically, the Jarque-Bera （JB） test is conducted to examine the normality of Wi-Fi signal distribution at each Reference Point （RP）. Then, according to the different Wi-Fi signal distributions, the hybrid Mann-Whitney U test and T test approaches are used to construct the set of matching reference points with the purpose of realizing the area localization. Finally, by calculating the K-Nearest Neighbor （KNN） of matching reference points in the located area, the location coordinate of the target is obtained. The experimental results indicate that the proposed approach is featured with higher localization accuracy as well as stronger system robustness compared with the conventional Wi-Fi indoor localization approaches.