Radio map construction requires a large amount of radio measurement data with
location labels, which imposes a high deployment cost. This paper develops a
region-based radio map from received signal strength (RSS) measurements without
location labels. The construction is based on a set of blindly collected RSS
measurement data from a device that visits each region in an indoor area
exactly once, where the footprints and timestamps are not recorded. The main
challenge is to cluster the RSS data and match clusters with the physical
regions. Classical clustering algorithms fail to work as the RSS data naturally
appears as non-clustered due to multipaths and noise. In this paper, a signal
subspace model with a sequential prior is constructed for the RSS data, and an
integrated segmentation and clustering algorithm is developed, which is shown
to find the globally optimal solution in a special case. Furthermore, the
clustered data is matched with the physical regions using a graph-based
approach. Based on real measurements from an office space, the proposed scheme
reduces the region localization error by roughly 50% compared to a weighted
centroid localization (WCL) baseline, and it even outperforms some supervised
localization schemes, including k-nearest neighbor (KNN), support vector
machine (SVM), and deep neural network (DNN), which require labeled data for
training.

使用无需位置标签的接收信号强度（RSS）测量数据，本文开发了一个基于区域的无线电地图构建方法，并提出了集成分割和聚类算法来解决 RSS 数据的聚类和区域匹配问题，结果表明该方案能够将区域定位误差降低约 50%，甚至优于一些需要标记数据进行训练的监督定位方案，如最近邻（KNN）、支持向量机（SVM）和深度神经网络（DNN）