Robust Multi-Dimensional Harmonic Retrieval Using Iteratively Reweighted HOSVD
Fuxi Wen, Hing Cheung So · IEEE Signal Processing Letters · 2015
Higher-order singular value decomposition (HOSVD) is usually required in$R$-dimensional ($R$-D) harmonic retrieval, where$R \geq 3$. In this letter, we devise an iteratively reweighted HOSVD technique, which is referred to as IR-HOSVD, for multi-dimensional frequency estimation in the presence of impulsive noise. The main idea is to minimize the${\ell _p}$-norm residual errors along all the$R$dimensions, where$1 < p < 2$. After decomposition, standard subspace techniques can be applied for parameter estimation. Based on the numerical results, IR-HOSVD outperforms several state-of-the-art techniques in terms of root mean square frequency error for different impulsive noise models.