A Mixed Approach to Similarity Metric Selection in Affinity Propagation-Based WiFi Fingerprinting Indoor PositioningReportar como inadecuado




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Department of Information Engineering, Electronics and Telecommunications DIET, Sapienza University of Rome, Via Eudossiana 18, 00184, Rome, Italy





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Academic Editor: Sisi Zlatanova

Abstract The weighted k-nearest neighbors WkNN algorithm is by far the most popular choice in the design of fingerprinting indoor positioning systems based on WiFi received signal strength RSS. WkNN estimates the position of a target device by selecting k reference points RPs based on the similarity of their fingerprints with the measured RSS values. The position of the target device is then obtained as a weighted sum of the positions of the k RPs. Two-step WkNN positioning algorithms were recently proposed, in which RPs are divided into clusters using the affinity propagation clustering algorithm, and one representative for each cluster is selected. Only cluster representatives are then considered during the position estimation, leading to a significant computational complexity reduction compared to traditional, flat WkNN. Flat and two-step WkNN share the issue of properly selecting the similarity metric so as to guarantee good positioning accuracy: in two-step WkNN, in particular, the metric impacts three different steps in the position estimation, that is cluster formation, cluster selection and RP selection and weighting. So far, however, the only similarity metric considered in the literature was the one proposed in the original formulation of the affinity propagation algorithm. This paper fills this gap by comparing different metrics and, based on this comparison, proposes a novel mixed approach in which different metrics are adopted in the different steps of the position estimation procedure. The analysis is supported by an extensive experimental campaign carried out in a multi-floor 3D indoor positioning testbed. The impact of similarity metrics and their combinations on the structure and size of the resulting clusters, 3D positioning accuracy and computational complexity are investigated. Results show that the adoption of metrics different from the one proposed in the original affinity propagation algorithm and, in particular, the combination of different metrics can significantly improve the positioning accuracy while preserving the efficiency in computational complexity typical of two-step algorithms. View Full-Text

Keywords: WiFi fingerprinting indoor positioning; similarity metrics; WkNN; affinity propagation WiFi fingerprinting indoor positioning; similarity metrics; WkNN; affinity propagation





Autor: Giuseppe Caso * , Luca de Nardis and Maria-Gabriella di Benedetto

Fuente: http://mdpi.com/



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