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Robust Multi-Dimensional Harmonic Retrieval Using Iteratively Reweighted HOSVD
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DOI：10.1109/LSP.2015.2493521.png)
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Abstract
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Higher-order singular value decomposition (HOSVD) is usually required in R-dimensional (R-D) harmonic retrieval, where R >= 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 l(p)-norm residual errors along all the 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.
Keywords:
Harmonic retrieval
higher-order singular value decomposition
l(p)-norm
parameter estimation
tensor
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