Constrained Cramér-Rao Bound of Multilinear Singular Value Decomposition

README file includes the content and instructions. Generates a 3D tensor, calculates the oracle bound and the constrained cramer rao bound. Prints the singular values of the generated tensor, and the condition number of \mat{V}^T\Omega\mat{V}. Runs the simulation for 1000 Monte Carlo simulations, and creates the plot CRB_plot.png. Abstract: Tensor decomposition methods for signal processing applications are an active area of research. Real data are often low-rank, noisy, and come in a higher-order format. As such, low-rank tensor approximation methods that account for the multidimensional structure of the data are often used for denoising. One way to represent a tensor in a low-rank form is to decompose the tensor into a set of orthonormal factor matrices and an all-orthogonal core tensor using multi-linear singular value decomposition. Under noisy measurements, the lower bound for recovering the factor matrices and the core tensor is unknown. In this paper, we exploit the well-studied constrained Cramér-Rao bound to reconstruct the components of the multilinear singular value decomposition under additive white Gaussian noise, and we validate our approach through simulation.