TY - JOUR
T1 - Accurate WiFi localization by unsupervised fusion of extended candidate location set
AU - Guo, Xiansheng
AU - Zhu, Shilin
AU - Li, Lin
AU - Hu, Fangzi
AU - Ansari, Nirwan
N1 - Funding Information:
Manuscript received July 7, 2018; revised August 21, 2018; accepted September 11, 2018. Date of publication September 17, 2018; date of current version May 8, 2019. This work was supported in part by the National Natural Science Foundation of China under Grant 61371184, Grant 61671137, Grant 61771114, and Grant 61771316, and in part by the Fundamental Research Funds for the Central Universities under Grant ZYGX2016J028. (Corresponding author: Xiansheng Guo.) X. Guo, S. Zhu, L. Li, and F. Hu are with the Department of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China (e-mail: xsguo@uestc.edu.cn; horizon_z40@foxmail.com; 986381283@qq.com; linli9419@gmail.com).
Publisher Copyright:
© 2014 IEEE.
PY - 2019/4
Y1 - 2019/4
N2 - Fusing the predictions of multiple received signal strength (RSS)-based classifiers is an efficient strategy to mitigate the impact of the fluctuation of the RSS. However, most of the existing fusion methods exhibit two remarkable shortcomings: 1) they need to train and store offline weights by the supervised learning and 2) they directly fuse the so-called candidate location set (CLS), which is collected from the most likely location estimate of each classifier (location with the largest probability of being the true location predicted by the classifier), and thus do not fully leverage the knowledge of classifiers. In general, the fluctuation of RSS does not guarantee the location predicted by each classifier with the highest probability to be the true location, thus leading to severe performance degeneration of the existing fusion methods. To overcome the above shortcomings, we propose an accurate WiFi localization framework by unsupervised fusion of an extended CLS (ECLS). First, we train multiple classifiers by only using RSS fingerprints in the offline phase. In the online phase, instead of collecting the CLS from the trained classifiers, we construct an ECLS by augmenting CLS with other location estimates (locations with predication probability greater than a certain threshold) from each classifier. As compared with the CLS, ECLS provides a bigger fusion space that likely includes the true location of the user. Furthermore, an unsupervised fusion localization algorithm based on the ECLS is derived from the joint optimization of weights and the location of the user. Furthermore, a point of inflection searching algorithm is also proposed to intelligently construct the ECLS. Real experimental results show that our proposed algorithm is more robust to changing environments and model errors, and can significantly improve the localization accuracy without any fingerprint and hardware calibrations.
AB - Fusing the predictions of multiple received signal strength (RSS)-based classifiers is an efficient strategy to mitigate the impact of the fluctuation of the RSS. However, most of the existing fusion methods exhibit two remarkable shortcomings: 1) they need to train and store offline weights by the supervised learning and 2) they directly fuse the so-called candidate location set (CLS), which is collected from the most likely location estimate of each classifier (location with the largest probability of being the true location predicted by the classifier), and thus do not fully leverage the knowledge of classifiers. In general, the fluctuation of RSS does not guarantee the location predicted by each classifier with the highest probability to be the true location, thus leading to severe performance degeneration of the existing fusion methods. To overcome the above shortcomings, we propose an accurate WiFi localization framework by unsupervised fusion of an extended CLS (ECLS). First, we train multiple classifiers by only using RSS fingerprints in the offline phase. In the online phase, instead of collecting the CLS from the trained classifiers, we construct an ECLS by augmenting CLS with other location estimates (locations with predication probability greater than a certain threshold) from each classifier. As compared with the CLS, ECLS provides a bigger fusion space that likely includes the true location of the user. Furthermore, an unsupervised fusion localization algorithm based on the ECLS is derived from the joint optimization of weights and the location of the user. Furthermore, a point of inflection searching algorithm is also proposed to intelligently construct the ECLS. Real experimental results show that our proposed algorithm is more robust to changing environments and model errors, and can significantly improve the localization accuracy without any fingerprint and hardware calibrations.
KW - Candidate location set (CLS)
KW - WiFi
KW - extended candidate location set (ECLS)
KW - indoor localization
KW - received signal strength (RSS)
KW - unsupervised fusion
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UR - http://www.scopus.com/inward/citedby.url?scp=85053292843&partnerID=8YFLogxK
U2 - 10.1109/JIOT.2018.2870659
DO - 10.1109/JIOT.2018.2870659
M3 - Article
AN - SCOPUS:85053292843
SN - 2327-4662
VL - 6
SP - 2476
EP - 2485
JO - IEEE Internet of Things Journal
JF - IEEE Internet of Things Journal
IS - 2
M1 - 8466583
ER -