RSS-based Indoor Passive Localization Using Clustering and Filtering in a LTE Network

Nowadays, fingerprint positioning is the mainstream method in indoor positioning and Weighted-K-Nearest-Neighbor (WKNN) is most widely used in fingerprint matching. However, the fingerprints which are far away from each other might also be similar and the target fingerprints have to match with all fingerprints every time, which results in unsatisfactory accuracy and efficiency of WKNN. In this paper, we propose an algorithm to classify regions with comprehensive consideration of geographic location and fingerprint similarity, meanwhile preprocessing and filtering the data are used to improve the accuracy. In addition, we propose that the cellular signal in Long Term Evolution (LTE) is more suitable to be the signal source in indoor passive localization scenario. Results show that in the LTE environment, our proposed algorithm effectively reduces the positioning error by about 24% and improves the convergence speed compared with WKNN.

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