Iteratively reweighted tensor SVD for robust multi-dimensional harmonic retrieval
Weize Sun, Xin Bo Lin, Hing Cheung So, Lei Huang, Qiang Li · 2016
In this paper, parameter estimation for multi-dimensional sinusoids in additive impulsive noise is addressed. Our underlying idea is to minimize the ℓp-norm of the residual error tensor, where 12-norm minimization. In doing so, we can utilize the tensorial structure of the received data and then apply iteratively reweighted tensor singular value decomposition, referred to as IR-t-SVD, to recover the subspace or the signal tensor. After the recovery step, standard subspace techniques can be applied for parameter estimation. Based on the numerical results, IR-t-SVD outperforms several state-of-the-art methods in terms of mean square frequency error under α-stable noise.