Open AccessDynamic threshold location algorithm based on fingerprinting method
Author(s) -
Ding Xuxing,
Wang Bingbing,
Wang Zaijian
Publication year - 2018
Publication title -
etri journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.295
H-Index - 46
eISSN - 2233-7326
pISSN - 1225-6463
DOI - 10.4218/etrij.2017-0155
Subject(s) - position (finance) , k nearest neighbors algorithm , algorithm , computer science , filter (signal processing) , artificial intelligence , computer vision , finance , economics
The weighted K ‐nearest neighbor ( WKNN ) algorithm is used to reduce positioning accuracy, as it uses a fixed number of neighbors to estimate the position. In this paper, we propose a dynamic threshold location algorithm ( DH ‐ KNN ) to improve positioning accuracy. The proposed algorithm is designed based on a dynamic threshold to determine the number of neighbors and filter out singular reference points ( RP s). We compare its performance with the WKNN and Enhanced K ‐Nearest Neighbor ( EKNN ) algorithms in test spaces of networks with dimensions of 20 m × 20 m, 30 m × 30 m, 40 m × 40 m and 50 m × 50 m. Simulation results show that the maximum position accuracy of DH ‐ KNN improves by 31.1%, and its maximum position error decreases by 23.5%. The results demonstrate that our proposed method achieves better performance than other well‐known algorithms.