Computationally efficient estimation of multi-dimensional spectral lines

摘要: In this work, we propose a computationally efficient algorithm for estimating multi-dimensional spectral lines. The method treats the data tensor's dimensions separately, yielding corresponding frequency estimates each dimension. Then, in second step, are ordered over dimensions, thus forming resulting multidimensional parameter estimates. For high dimensional data, proposed offers statistically moderate to signal noise ratios, at computational cost substantially lower than typical non-parametric Fourier-transform based periodogram solutions, as well state-of-the-art parametric estimators.