Abstract
We propose a novel algorithm based on the . The proposed algorithm can be divided into three steps, an offline phase at which an (AC) strategy is used, an online phase of approximate localization at which is used, and an online phase of precise localization with . Specifically, after offline fingerprint collection and similarity measurement, we employ an AC strategy based on the -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 . 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.
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