Details

Autor(en) / Beteiligte

• Zayyani, H.
• Babaie-zadeh, M.
• Haddadi, F.
• Jutten, C.

Titel

On the Cramér-Rao Bound for Estimating the Mixing Matrix in Noisy Sparse Component Analysis

Ist Teil von

• IEEE signal processing letters, 2008-01, Vol.15, p.609-612

Ort / Verlag

New York: IEEE

Erscheinungsjahr

2008

Quelle

IEEE/IET Electronic Library (IEL)

Beschreibungen/Notizen

• 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.