TY - JOUR
T1 - Localization by Fusing a Group of Fingerprints via Multiple Antennas in Indoor Environment
AU - Guo, Xiansheng
AU - Ansari, Nirwan
N1 - Funding Information:
Manuscript received May 9, 2017; revised July 17, 2017; accepted July 19, 2017. Date of publication July 25, 2017; date of current version November 10, 2017. This work is supported in part by the National Natural Science Foundation of China under Grant 61201277, Grant 61371184, and Grant 61671137) and in part by the Fundamental Research Funds for the Central Universities (No. ZYGX2016J028). The review of this paper was coordinated by Z. Yang. (Corresponding author: Xiansheng Guo.) X. Guo as a research scholar, visiting Advanced Networking Lab., Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA. He is now with the Department of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China (e-mail: xsguo@uestc.edu.cn).
Publisher Copyright:
© 1967-2012 IEEE.
PY - 2017/11
Y1 - 2017/11
N2 - Most existing fingerprints-based indoor localization approaches are based on some single fingerprint, such as received signal strength (RSS), channel impulse response, and signal subspace. However, the localization accuracy obtained by the single fingerprint approach is rather susceptible to the changing environment, multipath, and non-line-of-sight propagation. In this paper, we propose a novel localization framework by Fusing A Group Of fingerprinTs (FAGOT) via multiple antennas for the indoor environment. We first build a GrOup Of Fingerprints (GOOF), which includes five different fingerprints, namely, RSS, covariance matrix, signal subspace, fractional low-order moment, and fourth-order cumulant, which are obtained by different transformations of the received signals from multiple antennas in the offline stage. Then, we design a parallel GOOF multiple classifiers based on AdaBoost (GOOF-AdaBoost) to train each of these fingerprints in parallel as five strong multiple classifiers. In the online stage, we input the corresponding transformations of the real measurements into these strong classifiers to obtain independent decisions. Finally, we propose an efficient combination fusion algorithm, namely, MUltiple Classifiers mUltiple Samples (MUCUS) fusion algorithm to improve the accuracy of localization by combining the predictions of multiple classifiers with different samples. As compared with the single fingerprint approaches, our proposed approach can improve the accuracy and robustness of localization significantly. We demonstrate the feasibility and performance of the proposed algorithm through extensive simulations as well as via real experimental data using a Universal Software Radio Peripheral platform with four antennas.
AB - Most existing fingerprints-based indoor localization approaches are based on some single fingerprint, such as received signal strength (RSS), channel impulse response, and signal subspace. However, the localization accuracy obtained by the single fingerprint approach is rather susceptible to the changing environment, multipath, and non-line-of-sight propagation. In this paper, we propose a novel localization framework by Fusing A Group Of fingerprinTs (FAGOT) via multiple antennas for the indoor environment. We first build a GrOup Of Fingerprints (GOOF), which includes five different fingerprints, namely, RSS, covariance matrix, signal subspace, fractional low-order moment, and fourth-order cumulant, which are obtained by different transformations of the received signals from multiple antennas in the offline stage. Then, we design a parallel GOOF multiple classifiers based on AdaBoost (GOOF-AdaBoost) to train each of these fingerprints in parallel as five strong multiple classifiers. In the online stage, we input the corresponding transformations of the real measurements into these strong classifiers to obtain independent decisions. Finally, we propose an efficient combination fusion algorithm, namely, MUltiple Classifiers mUltiple Samples (MUCUS) fusion algorithm to improve the accuracy of localization by combining the predictions of multiple classifiers with different samples. As compared with the single fingerprint approaches, our proposed approach can improve the accuracy and robustness of localization significantly. We demonstrate the feasibility and performance of the proposed algorithm through extensive simulations as well as via real experimental data using a Universal Software Radio Peripheral platform with four antennas.
KW - AdaBoost
KW - group Of Fingerprints (GOOF)
KW - multiple Classifiers mUltiple Samples (MUCUS) fusion localization
KW - multiple antennas
KW - universal software radio peripheral (USRP)
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U2 - 10.1109/TVT.2017.2731874
DO - 10.1109/TVT.2017.2731874
M3 - Article
AN - SCOPUS:85028947921
SN - 0018-9545
VL - 66
SP - 9904
EP - 9915
JO - IEEE Transactions on Vehicular Technology
JF - IEEE Transactions on Vehicular Technology
IS - 11
M1 - 7990593
ER -