Mixed Near-field and Far-field Sources Localization via Second-order Statistics
Zhong-xi Xia, Xiaofei Zhang, Weitao Liu, Qianlin Cheng, Dong-lin Yang · 2017
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.