Semi-supervised Indoor Fingerprint Database Construction Method Based on the Nonhomogeneous Distribution Characteristic of Received Signal Strength
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摘要: 室内定位中半监督学习的指纹库构建方法能够降低人力开销，但忽略了高维接收信号强度(RSS)数据不均匀的非齐分布特点，影响定位精度，针对此问题该文提出一种基于RSS非齐性分布特征的半监督流形对齐指纹库构建方法。该算法运用局部RSS尺度参数以及共享近邻相似性构造权重矩阵，得到精确反映RSS数据流形结构的权重图，利用该权重图通过求解流形对齐的目标函数最优解，实现运用少量标记数据对大量未标记数据的位置标定。实验结果表明，该算法可以显著降低离线阶段数据采集的工作量，同时可以取得较高的定位精度。Abstract: The radio map construction is time consuming and labor intensive, and the conventional semi-supervised based methods usually ignore the influence of the uneven distribution of high-dimensional Received Signal Strength (RSS). In order to solve that problem, a semi-supervised radio map construction approach which is based on the nonhomogeneous distribution characteristic of RSS is proposed. The approach utilizes the RSS local scale and common neighbors similarities to calculate the weighted matrix. Thus, the weighted graph that reflects accurately the structure of RSS data manifold is presented. In addition, the weighted graph is used to find the optimal solution of the objective function to calibrate the locations of plenty of unlabeled data by a small number of labeled RSS. The extensive experiments demonstrate that the proposed method is capable of not only constructing an accurate radio map at a low manual cost, but also achieving a high localization accuracy.
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