Robust R-D parameter estimation via closed-form PARAFAC in Kronecker colored environments
João Paulo C. L. da Costa, Dominik Schulz, Florian Roemer, Martin Haardt, José A. Apolinário · 2010
To estimate parameters from measurements sampled on a multidimensional grid, Parallel Factor Analysis (PARAFAC) based schemes are very appealing, since they are applicable to mixed array geometries, which are a mixture of arbitrary arrays and outer product based arrays. Moreover, for PARAFAC based schemes, errors in some dimensions do not affect the estimation of parameters in the other dimensions. Particularly, the closed-form PARAFAC based parameter estimator has the additional advantage of being non-iterative. In this contribution, we propose a PARAFAC decomposition for colored noise with a Kronecker structure called Sequential Generalized Singular Value Decomposition (S-GSVD) based closed-form PARAFAC. Our proposed estimator joins the advantages of the closed-form PARAFAC - such as the applicability to mixed array geometries and the robustness to arrays with positioning errors - with the suitability of the S-GSVD for data contaminated by Kronecker colored noise or interference.