TY - JOUR
T1 - Knowledge Aided Adaptive Localization via Global Fusion Profile
AU - Guo, Xiansheng
AU - Li, Lin
AU - Ansari, Nirwan
AU - Liao, Bin
N1 - Funding Information:
Manuscript received September 28, 2017; revised November 24, 2017; accepted December 22, 2017. Date of publication December 27, 2017; date of current version April 10, 2018. This work was supported in part by the National Science Foundation (NSF) 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 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:
© 2017 IEEE.
PY - 2018/4
Y1 - 2018/4
N2 - Indoor localization is becoming critical to empower Internet of Things for various applications, such as asset tracking, geolocation, and smart cities. Wi-Fi-based indoor localization using received signal strength (RSS) has drawn much attention over the past decade because it does not require extra infrastructure and specialized hardware. It is well known that the localization accuracy using RSS is rather susceptible to the changing environment. Localization by fusing multiple fingerprint functions of RSS is a promising strategy to overcome the above drawback. However, the existing fusion techniques cannot make full use of the intrinsic complementarity among multiple fingerprint functions. It also fails to exploit the knowledge obtained in the offline phase and thus shows low accuracy in the complex environment. This paper proposes a knowledge aided adaptive localization (KAAL) approach by using a global fusion profile (GFP) to mitigate the above shortcomings. First, we propose a GFP construction algorithm by minimizing position errors over all fingerprint functions with weight constraints in the offline phase. Based on the knowledge from GFP and the trained multiple fingerprint models, we then derive two KAAL algorithms, namely, multiple function averaging and optimal function selection, to achieve highly accurate localization results. Experimental results demonstrate that our proposed localization approach is superior to the existing methods both in simulated and real environments.
AB - Indoor localization is becoming critical to empower Internet of Things for various applications, such as asset tracking, geolocation, and smart cities. Wi-Fi-based indoor localization using received signal strength (RSS) has drawn much attention over the past decade because it does not require extra infrastructure and specialized hardware. It is well known that the localization accuracy using RSS is rather susceptible to the changing environment. Localization by fusing multiple fingerprint functions of RSS is a promising strategy to overcome the above drawback. However, the existing fusion techniques cannot make full use of the intrinsic complementarity among multiple fingerprint functions. It also fails to exploit the knowledge obtained in the offline phase and thus shows low accuracy in the complex environment. This paper proposes a knowledge aided adaptive localization (KAAL) approach by using a global fusion profile (GFP) to mitigate the above shortcomings. First, we propose a GFP construction algorithm by minimizing position errors over all fingerprint functions with weight constraints in the offline phase. Based on the knowledge from GFP and the trained multiple fingerprint models, we then derive two KAAL algorithms, namely, multiple function averaging and optimal function selection, to achieve highly accurate localization results. Experimental results demonstrate that our proposed localization approach is superior to the existing methods both in simulated and real environments.
KW - Global fusion profile (GFP)
KW - Wi-Fi
KW - indoor localization
KW - knowledge aided adaptive localization (KAAL)
KW - received signal strength (RSS)
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U2 - 10.1109/JIOT.2017.2787594
DO - 10.1109/JIOT.2017.2787594
M3 - Article
AN - SCOPUS:85040034907
SN - 2327-4662
VL - 5
SP - 1081
EP - 1089
JO - IEEE Internet of Things Journal
JF - IEEE Internet of Things Journal
IS - 2
ER -