自适应AP选择无线室内定位算法

针对接收信号强度的无线室内定位算法中无线接入点（AP）具有相关性和区域性问题，提出自适应AP选择无线室内定位算法。针对AP具有相关性，在线阶段提出基于互信息和信息熵的AP选择算法，联合衡量AP子集内所有AP间的相关性，搜索包含有效位置信息最多的AP子集。对于AP的区域性问题，离线阶段对参考点高斯邻域内原始RSS采样数据进行最小二乘高斯曲线拟合，并提出优先度函数衡量AP性能。最后，采用KL散度实现待定位节点位置估计。与WKNN、MLE算法相比，该算法有效地提高了无线室内定位精度。

Wireless indoor localization algorithm based on adaptive selection of access point

Aiming at solving the problem that access points of wireless indoor location have correlation and regional, a novel improved algorithm called OCUMI （Online Continuous source of Union Mutual Information） is proposed. Considering the correlation between the access points, a self-adaptive access point selection algorithm based on mutual information and entropy is proposed in online phase. The correlation between access points is measured through continuous source joint mutual information； at the same time, combining the information entropy to search the access points which contain most valid information about the location. Finally, Kullback-Leibler divergence is used to match position, and estimate the node position. The performance evaluation is conducted at the premises of a research laboratory and a conference room under real-life conditions. Experimental results show that, compared with WKNN and MLE, the proposed OCUMI algorithm has better localization accuracy.