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中文摘要：
针对传统无线局域网（WLAN）室内定位系统中因参考点密集分布及逐点信号采集所带来的位置指纹数据库构建工作量繁重的问题,该文提出一种基于混合半监督流形学习和3次样条插值的数据库构建方法。该方法利用少量标记数据和大量未标记数据求解定位目标函数的最优解,同时根据高维信号强度空间与低维物理位置空间的映射关系,实现对未标记数据的位置标定。大量实验结果表明,该方法能够在保证较高定位精度的同时,显著降低位置指纹数据库的构建开销。
英文摘要：
To deal with the high cost involved in the location fingerprint database construction due to the dense Reference Points （RPs） distribution and point-by-point Received Signal Strength （RSS） collection in the conventional Wireless Local Area Network （WLAN） indoor localization systems, a new database construction approach based on the integrated semi-supervised manifold learning and cubic spline interpolation is proposed. The proposed approach utilizes a small amount of labeled data and a massive amount of unlabeled data to find the optimal solution to localization target function, and meanwhile relies on the mapping relations between the high-dimensional signal strength space and low-dimensional physical location space to calibrate the unlabeled data with location coordinates. The extensive experiments demonstrate that the proposed approach is able to guarantee the high localization accuracy, as well as significantly reduce the cost involved in location fingerprint database construction.
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