A map based estimator for inverse complex covariance matricies
Paper in proceeding, 2012

A novel approach to estimate (inverse) complex covariance matrices is proposed. By considering the class of unitary invariant estimators, the main challenge lies in estimating the underlying eigenvalues from sampled versions. By exploiting that the distribution of the sample eigenvalues can be derived in closed form, a Maximum A Posteriori (MAP) based scheme is then derived. The performance of the derived estimator is simulated and results indicate that the proposed scheme shows performance similar to one of the best estimators known to date. The main advantage lies in that the proposed solution only requires numerical optimization over a P-dimensional space where P is the size of the covariance matrix.

Maximum a posteriori

Closed form

Numerical optimizations

Eigenvalues

Sample eigenvalues

Complex covariance

Author

Magnus Nordenvaad

Uppsala University

Lennart Svensson

Chalmers, Signals and Systems, Signal Processing and Biomedical Engineering

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

15206149 (ISSN)

3369-3372 6288638
978-1-4673-0046-9 (ISBN)

Subject Categories

Signal Processing

DOI

10.1109/ICASSP.2012.6288638

ISBN

978-1-4673-0046-9

More information

Latest update

2/28/2018