About Noneigenvector Source Localization Methods

摘要: Previous studies dedicated to source localization are based on the spectral matrix algebraic properties. In particular, two noneigenvector methods, namely, propagator and Ermolaev Gershman (EG) algorithms, exhibit a low computational load. Both methods structure. The first method is partitioning. second one obtains directly an approximation of noise subspace using adjustable power parameter choosing threshold value. It has been shown that these algorithms efficient in nonnoisy or high signal ratio (SNR) environments. However, both will be improved. Firstly, not robust noise. Secondly, EG algorithm requires knowledge value between largest smallest eigenvalues, which available as eigendecomposition, performed. this paper, we aim firstly at demonstrating usefulness QR LU factorizations for secondly propose new way reduce load resolution by estimating only needed eigenvectors. For this, adapt fixed-point compute leading We evaluate performance proposed comparative study.