Indoor Localization Technology (ILT) based on Wi-Fi network has been rapidly developed with high localization accuracy and low hardware requirements. Collecting the Received Signal Strength (RSS) samples to construct the fingerprint database, however, is time consuming and labor intensive, hindering the application of the technology. An Indoor Localization Method is proposed based on Co-Forest and Bayesian Compressed Sensing (ILM-CFBCS), utilizing the crowdsourcing network technology to collect RSS data and the min-max method to preprocess the data so as to establish the indoor fingerprint database. The user's position is determined according to the decision result of the random forest classifier trained by the Co-Forest algorithm combining with majority principle. Finally, a constructing method of offline fingerprint database is put forward by combining the similarity between Bayesian compressed sensing theory and reference point fingerprint to realize the fingerprint database update. The experimental results show that the proposed method can achieve good localization performance by using a small amount of data with labeled positions.

基于Wi-Fi网络的室内定位技术（ILT）已得到快速发展，具有很高的定位精度和较低的硬件要求。但是，收集接收信号强度（RSS）样本以构建指纹数据库非常耗时且费力，这阻碍了该技术的应用。提出了一种基于Co-Forest和贝叶斯压缩感知技术（ILM-CFBCS）的室内定位方法，利用众包网络技术收集RSS数据，采用min-max方法对数据进行预处理，建立室内指纹数据库。用户位置是根据Co-Forest算法结合多数原则训练的随机森林分类器的决策结果确定的。最后，结合贝叶斯压缩感知理论与参考点指纹的相似性，提出离线指纹数据库的构建方法，以实现指纹数据库的更新。实验结果表明，该方法通过使用少量带有标记位置的数据可以实现良好的定位性能。