We propose a novel indoor positioning algorithm based on the received signal strength (RSS) fingerprint. The proposed algorithm can be divided into three steps, an offline phase at which an advanced clustering (AC) strategy is used, an online phase of approximate localization at which cluster matching is used, and an online phase of precise localization with kernel ridge regression. Specifically, after offline fingerprint collection and similarity measurement, we employ an AC strategy based on the K-medoids clustering algorithm using additional reference points that are geographically located at the outer cluster boundary to enrich the data of each cluster. During the approximate localization, RSS measurements are compared with the cluster radio maps to determine to which cluster the target most likely belongs. Both the Euclidean distance of the RSSs and the Hamming distance of the coverage vectors between the observations and training records are explored for cluster matching. Then, a kernel-based ridge regression method is used to obtain the ultimate positioning of the target. The performance of the proposed algorithm is evaluated in two typical indoor environments, and compared with those of state-of-the-art algorithms. The experimental results demonstrate the effectiveness and advantages of the proposed algorithm in terms of positioning accuracy and complexity.

我们提出了一种基于接收信号强度（RSS）指纹的新型室内定位算法。所提出的算法可以分为三个步骤：使用高级聚类（AC）策略的脱机阶段，使用聚类匹配的近似本地化的在线阶段以及使用内核岭回归进行精确定位的在线阶段。具体来说，在离线指纹收集和相似度测量之后，我们采用基于K的AC策略-medoids聚类算法使用地理上位于外部聚类边界的其他参考点来丰富每个聚类的数据。在近似定位期间，将RSS测量值与群集无线电图进行比较，以确定目标最有可能属于哪个群集。探索RSS的欧几里得距离和观测记录与训练记录之间的覆盖向量的汉明距离，以进行聚类匹配。然后，使用基于核的岭回归方法来获得目标的最终定位。在两种典型的室内环境中评估了所提出算法的性能，并与最新算法进行了比较。