Algorithms and performance analysis for estimation of low-rank matrices with Kronecker structured singular vectors

We consider the problem of estimating the singular vectors of low-rank signal matrices buried in noise in the setting where the singular vectors are assumed to be Kronecker products of unknown vectors. We propose four algorithms for estimating such singular vectors, analyze their performance and show that they asymptotically fail to estimate to latent singular vector below the same critical SNR. We corroborate our theoretical findings with numerical simulations and illustrate the improved performance on a STAP beamforming application.