基于多维模糊映射AP优化的WLAN室内定位方法

Multi-Dimensional Fuzzy Mapping for AP Optimization Based WLAN Indoor Localization

室内定位技术在多领域有着重要的应用,而传统的无线局域网(Wireless Local Area Network,WLAN)指纹定位方法通常很少考虑WLAN接收信号强度(Received Signal Strength,RSS)特征的多样性以及来自不同接入点(Access Point,AP)的RSS特征位置分辨力的差异性问题,从而导致WLAN定位精度不高且定位效率较低.对此,本文提出一种基于多维模糊映射AP优化的WLAN室内定位方法.在离线阶段通过多次采集RSS数据提取多维RSS特征,计算AP信息增益比及相应的离线模糊隶属度,并利用模糊关系方程求解多维RSS特征模糊权重;而在在线阶段,则通过多维模糊映射构造模糊判定矩阵并计算AP在线模糊隶属度,同时结合K近邻(K-Nearest Neighbor,KNN)算法完成对目标的位置坐标计算.实验结果表明,相较于传统的AP优化定位方法,所提方法在线阶段的定位计算开销最高减少了4.12 s,定位误差4 m内的置信概率为91.91%.

The indoor localization technology has important applications in many fields,while traditional wireless local area network(WLAN)fingerprint-based localization methods usually rarely consider both the diversity of WLAN received signal strength(RSS)features and the difference of the position resolution of RSS features from different access points(APs),which results in the low localization accuracy and efficiency.To address this problem,this paper proposes a WLAN indoor localization method based on the multi-dimensional fuzzy mapping for the AP optimization.Specifically,in the offline phase,the information gain ratio of the AP and the corresponding offline fuzzy membership degree are calculated according to the multi-dimensional RSS features which are extracted many times,and meanwhile the fuzzy relationship equation is utilized to solve out fuzzy weights of multi-dimensional RSS features.In the online phase,the fuzzy decision matrix is constructed by the multi-dimensional fuzzy mapping to calculate the online fuzzy membership degree of the AP,and then the target location estimation is realized by combining with the K-nearest neighbor(KNN)algorithm.Experimental results show that compared with the traditional AP optimization based localization methods,the localization calculation overhead in the online stage of the proposed method is reduced by up to 4.12 s,and the confidence probability of the positioning error within 4 meters is 91.91%.