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Indoor Source Localization Using 2D Multi-Sensor Based Spatial Spectrum Fusion Algorithm

Authors:
Taha Bouras
Di He
Wenxian Yu
Yi Zhang

Keywords: 2-D Localization; Multi-Sensor; Spatial Spectrum Estimation Techniques; Data Fusion.

Abstract:
In a starving indoor environment where non-line of sight (NLOS) signals are strongly dominant, localization using traditional spatial spectrum estimation techniques easily fails due to low signal to noise ratio (SNR). Accordingly, in this paper, a novel 2-D multi-sensor Spatial Spectrum Fusion (2D-SSF) localization algorithm based on the multiple signal classification (MUSIC) method is proposed. The output data of each uniform rectangular array (URA) at each access point (AP) are first processed to get the noise subspace data. Then, after estimating the corresponding azimuth and elevation angles of each array using the MUSIC approach and finding the position of each point relative to each sensor in the search grid with the help of grid refinement algorithm, the parameters of interest of the target are estimated from a single spectra that results from fusing all maximum noise subspaces where the position corresponding to the minimum error between the set of angles and every estimated point in the searching area is situated. Different simulation results of the proposed method in terms of RMSE as a function of SNR for various APs LOS/NLOS scenarios, the change in the number of antennas at each AP and the comparison with the MUSIC approach and the 1D localization based spatial spectrum fusion algorithm are carried out. The obtained results prove the significant performance of the proposed 2D-SSF localization algorithm with the strong presence of NLOS signals.

Pages: 79 to 84

Copyright: Copyright (c) IARIA, 2017

Publication date: November 12, 2017

Published in: conference

ISSN: 2308-4278

ISBN: 978-1-61208-598-2

Location: Barcelona, Spain

Dates: from November 12, 2017 to November 16, 2017