5/10/2023 0 Comments Affinity designer download gratisFlat and two-step W kNN share the issue of properly selecting the similarity metric so as to guarantee good positioning accuracy: in two-step W kNN, in particular, the metric impacts three different steps in the position estimation, that is cluster formation, cluster selection and RP selection and weighting. Only cluster representatives are then considered during the position estimation, leading to a significant computational complexity reduction compared to traditional, flat W kNN. Two-step W kNN 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. The position of the target device is then obtained as a weighted sum of the positions of the k RPs. W kNN 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 weighted k-nearest neighbors (W kNN) algorithm is by far the most popular choice in the design of fingerprinting indoor positioning systems based on WiFi received signal strength (RSS).
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