TY - JOUR
T1 - Accurate WiFi Localization by Fusing a Group of Fingerprints via a Global Fusion Profile
AU - Guo, Xiansheng
AU - Li, Lin
AU - Ansari, Nirwan
AU - Liao, Bin
N1 - Funding Information:
Manuscript received October 28, 2017; revised January 31, 2018 and April 2, 2018; accepted May 1, 2018. Date of publication May 3, 2018; date of current version August 13, 2018. 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. The review of this paper was coordinated by Prof. G. Mao. (Corresponding author: Nirwan Ansari.) X. Guo and L. Li are with the Department of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China (e-mail:,xsguo@uestc.edu.cn; linli9419@gmail.com).
Publisher Copyright:
© 1967-2012 IEEE.
PY - 2018/8
Y1 - 2018/8
N2 - The existing indoor localization approaches based on single fingerprints, such as received signal strength (RSS) and channel impulse response, are rather susceptible to the changing environment, multipath, and nonline-of-sight. It is well known that indoor localization can obtain higher positioning accuracy than the single-fingerprint-based methods by fusing multiple information sources (fingerprints/fingerprint functions). However, the existing fusion methods cannot fully exploit the intrinsic complementarity among multiple information sources and thus show lower accuracy. In this paper, we propose an accurate WiFi localization approach by Fusing A Group Of fingerprinTs (WiFi-FAGOT) via a global fusion profile (GFP). WiFi-FAGOT first constructs a WiFi-based GrOup Of Fingerprints (GOOF) in the offline phase, which consists of RSS, signal strength difference, and hyperbolic location fingerprint. Then, instead of direct localization by using the WiFi-based GOOF, we design multiple classifiers by training each fingerprint in the WiFi-based GOOF, namely GOOF classifiers. To fully leverage the intrinsic complementarity among different kinds of fingerprints, we propose a GFP construction algorithm by minimizing the average positioning error over the space of all GOOF classifiers. Finally, in the online phase, we derive a grid-dependent matching algorithm, namely, optimal classifier selection, to intelligently choose a fusion profile in the GFP for more accurate localization. Experimental results demonstrate that WiFi-FAGOT performs better than other systems in real complex indoor environments.
AB - The existing indoor localization approaches based on single fingerprints, such as received signal strength (RSS) and channel impulse response, are rather susceptible to the changing environment, multipath, and nonline-of-sight. It is well known that indoor localization can obtain higher positioning accuracy than the single-fingerprint-based methods by fusing multiple information sources (fingerprints/fingerprint functions). However, the existing fusion methods cannot fully exploit the intrinsic complementarity among multiple information sources and thus show lower accuracy. In this paper, we propose an accurate WiFi localization approach by Fusing A Group Of fingerprinTs (WiFi-FAGOT) via a global fusion profile (GFP). WiFi-FAGOT first constructs a WiFi-based GrOup Of Fingerprints (GOOF) in the offline phase, which consists of RSS, signal strength difference, and hyperbolic location fingerprint. Then, instead of direct localization by using the WiFi-based GOOF, we design multiple classifiers by training each fingerprint in the WiFi-based GOOF, namely GOOF classifiers. To fully leverage the intrinsic complementarity among different kinds of fingerprints, we propose a GFP construction algorithm by minimizing the average positioning error over the space of all GOOF classifiers. Finally, in the online phase, we derive a grid-dependent matching algorithm, namely, optimal classifier selection, to intelligently choose a fusion profile in the GFP for more accurate localization. Experimental results demonstrate that WiFi-FAGOT performs better than other systems in real complex indoor environments.
KW - FAGOT
KW - KNN
KW - WiFi
KW - global fusion profile (GFP)
KW - group of fingerprints (GOOF)
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U2 - 10.1109/TVT.2018.2833029
DO - 10.1109/TVT.2018.2833029
M3 - Article
AN - SCOPUS:85046485288
SN - 0018-9545
VL - 67
SP - 7314
EP - 7325
JO - IEEE Transactions on Vehicular Communications
JF - IEEE Transactions on Vehicular Communications
IS - 8
M1 - 8353839
ER -