Mixed Near-field and Far-field Sources Localization via Second-order Statistics
Zhong-xi Xia, Xiao-fei Zhang, Wei-tao Liu, Qian-lin Cheng, Dong-lin Yang
Available Online June 2016.
- https://doi.org/10.2991/mecs-17.2017.138How to use a DOI?
- Mixed near-field and far-field, localization, Second-order Statistics.
- 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.
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 -