Proceedings of the 2017 2nd International Conference on Machinery, Electronics and Control Simulation (MECS 2017)

Mixed Near-field and Far-field Sources Localization via Second-order Statistics

Authors
Zhong-xi Xia, Xiao-fei Zhang, Wei-tao Liu, Qian-lin Cheng, Dong-lin Yang
Corresponding Author
Zhong-xi Xia
Available Online June 2016.
DOI
https://doi.org/10.2991/mecs-17.2017.138How to use a DOI?
Keywords
Mixed near-field and far-field, localization, Second-order Statistics.
Abstract
This paper proposes an algorithm for mixed near-field and far-field sources localization, using the trilinear decomposition (PARAFAC) model via second-order statistics of the received signal. We construct two second order statistical matrices of the received signal and use PARAFAC model to obtain the parameters of all sources, then according to the definition of distance of near-field source, that we can correctly distinguish the near-field and far-field sources, and we can get the exact parameters estimation of all the sources. This method does not need eigenvalue decomposition of the covariance matrix of the received signal, and does not need to airspace traverse search, so it greatly reduces the computational complexity and automatically matches the parameters, avoiding the parameter matching process. MATLAB simulation results show that this is an effective parameter estimation algorithm for mixed near-field and far-field sources localization.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Cite this article

TY  - CONF
AU  - Zhong-xi Xia
AU  - Xiao-fei Zhang
AU  - Wei-tao Liu
AU  - Qian-lin Cheng
AU  - Dong-lin Yang
PY  - 2016/06
DA  - 2016/06
TI  - Mixed Near-field and Far-field Sources Localization via Second-order Statistics
BT  - Proceedings of the 2017 2nd International Conference on Machinery, Electronics and Control Simulation (MECS 2017)
PB  - Atlantis Press
SP  - 212
EP  - 217
SN  - 2352-5401
UR  - https://doi.org/10.2991/mecs-17.2017.138
DO  - https://doi.org/10.2991/mecs-17.2017.138
ID  - Xia2016/06
ER  -