A map based estimator for inverse complex covariance matricies
2012 (English)In: 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2012, 3369-3372 p.Conference paper (Refereed)
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.
Place, publisher, year, edition, pages
2012. 3369-3372 p.
Closed form, Complex covariance, Eigenvalues, Maximum a posteriori, Numerical optimizations, Sample eigenvalues, Covariance matrix, Eigenvalues and eigenfunctions, Signal processing, Estimation
IdentifiersURN: urn:nbn:se:uu:diva-186827DOI: 10.1109/ICASSP.2012.6288638ISI: 000312381403111ISBN: 978-1-4673-0046-9OAI: oai:DiVA.org:uu-186827DiVA: diva2:575223
2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012, 25- 30 March 2012, Kyoto, Japan