On the Cramér-Rao Bound for Estimating the Mixing Matrix in Noisy Sparse Component Analysis
Hadi Zayyani, Massoud Babaie‐Zadeh, Farzan Haddadi, Christian Jutten · IEEE Signal Processing Letters · 2008
In this letter, we address the theoretical limitations in estimating the mixing matrix in noisy sparse component analysis (SCA) for the two-sensor case. We obtain the Cramer-Rao lower bound (CRLB) error estimation of the mixing matrix. Using the Bernouli-Gaussian (BG) sparse distribution, and some simple assumptions, an approximation of the Fisher information matrix (FIM) is calculated. Moreover, this CRLB is compared to some of the main methods of mixing matrix estimation in the literature.