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.

翻译：无线电地图的构建需要大量带有位置标签的无线电测量数据，这导致部署成本高昂。本文利用无位置标签的接收信号强度（RSS）测量值，开发了一种基于区域的无线电地图。该地图构建基于一组来自设备的盲采集RSS测量数据（设备恰好依次访问室内各区域一次，且未记录行进轨迹与时间戳）。主要挑战在于对RSS数据进行聚类，并将聚类结果与物理区域匹配。由于多径效应和噪声，RSS数据天然呈现非聚类特征，经典聚类算法无法适用。本文为RSS数据构建了带顺序先验信息的信号子空间模型，并开发了集成式分割与聚类算法。研究表明，该算法在特殊情况下可找到全局最优解。进一步地，采用基于图的方法将聚类数据与物理区域匹配。基于办公空间的实测数据，与加权质心定位（WCL）基准方法相比，所提方案将区域定位误差降低约50%，甚至优于部分需要标注数据训练的监督式定位方案，包括k近邻（KNN）、支持向量机（SVM）和深度神经网络（DNN）。