Iterative Sequential GSVD (I-S-GSVD) based prewhitening for multidimensional HOSVD based subspace estimation without knowledge of the noise covariance information

Abstract

Recently, the Sequential GSVD (S-GSVD) based prewhitening scheme has been proposed to improve R-dimensional subspace-based parameter estimation schemes in the presence of colored noise or interference with Kronecker structure. To apply the S-GSVD, second order statistics of the noise should be estimated, e.g., via samples captured in the absence of the desired signal components. In this contribution, we propose the Iterative Sequential Generalized Singular Value Decomposition (I-S-GSVD) based prewhitening scheme for multidimensional HOSVD based subspace estimation when information about the noise statistics is not available. Even without the availability of samples in the absence of the desired signals components, it is possible to obtain the prewhitening correlation factors and the signal parameters in an iterative way using a deterministic algorithm in combination with the S-GSVD. This combination constitutes our proposed I-S-GSVD. Finally, the I-S-GSVD inherits the computational efficiency from the S-GSVD compared to matrix based prewhitening schemes.