期刊名称：International Journal of Distributed Sensor Networks
印刷版ISSN：1550-1329
电子版ISSN：1550-1477
出版年度：2014
卷号：2014
DOI：10.1155/2014/247525
出版社：Hindawi Publishing Corporation
摘要：Indoor localization based on existent WiFi signal
strength is becoming more and more prevalent and ubiquitous. Unfortunately, the WiFi received signal strength (RSS) is susceptible
by multipath, signal attenuation, and environmental changes,
which is the major challenge for accurate indoor localization. To overcome these limitations, we propose the cluster -nearest
neighbor (KNN) algorithm with 5 G WiFi signal to reduce the
environmental interference and improve the localization performance
without additional equipment. In this paper, we propose
three approaches to improve the performance of localization
algorithm. For one thing, we reduce the computation effort based
on the coarse localization algorithm. For another, according to
the detailed analysis of the 2.4 G and 5 G signal fluctuation,
we expand the real-time measurement RSS before matching the
fingerprint map. More importantly, we select the optimal nearest
neighbor points based on the proposed cluster KNN algorithm. We have implemented the proposed algorithm and evaluated
the performance with existent popular algorithms. Experimental
results demonstrate that the proposed algorithm can effectively
improve localization accuracy and exhibit superior performance
in terms of localization stabilization and computation effort.
