基于改进AP选择的融合随机森林室内定位算法

针对复杂室内环境下接收信号强度(RSS)值和维度发生变化的问题，提出一种改进的接入点(AP)选择方法并融合随机森林(RF)分类算法进行实时室内定位. 在离线阶段应用改进的AP选择方法，并使用AP的RSS数据方差以及AP出现频率来衡量AP稳定性并选取前m个稳定的AP. 在处理方差时会经拉普拉斯平滑，以避免出现方差为0的情况，并以此构建初步的指纹数据库；在在线阶段利用集成学习中的RF来对分类结果进行投票表决得到最终位置信息，并将改进后的算法同传统RF，改进后的AP选择融合加权的K近邻算法(WKNN)以及基于信息增益(IG)的AP选择算法加随机森林相比较. 实验结果表明:文中所提出的方法在定位误差方面较其他三个算法分别下降29.3%、23.2%、17.2%，同时在定位时间方面也有提升. 

Indoor location algorithm based on improved AP selection and random forest fusion

Aiming at the problem of the received signal strength (RSS) value and dimension change in complex indoor environment, an improved access point (AP) selection method and a random forest (RF) classification algorithm for real-time indoor location are proposed. The improved AP selection method in the off-line phase uses the RSS data variance of the AP and the AP appearance frequency to measure the AP stability and selects the first m stable APs. When the variance is processed, the Laplacian smoothing is performed to avoid the variance of 0, and construct a preliminary fingerprint database. The online phase uses the RF in the integrated learning to vote on the classification result to arrive at the final position. The improved algorithm is compared with the traditional random forest, the improved AP selection fusion weighted K-nearest neighbor algorithm (WKNN) and the information gain (IG)-based AP selection algorithm plus random forest. The experimental results show that the proposed method Compared with the other three algorithms, the positioning error decreased by 29.3%, 23.2%, and 17.2%, respectively, and the positioning time is also improved.