Referenced Items are not available for this document.
Computation of the Cramer-Rao bound (CRB) on estimator variance requires the inverse or the pseudo-inverse Fisher information matrix (FIM). Direct matrix inversion can be computationally intractable when the number of unknown parameters is large. In this correspondence, we compare several iterative methods for approximating the CRB using matrix splitting and preconditioned conjugate gradient algorithms. For a large class of inverse problems, we show that nonmonotone Gauss-Seidel and preconditioned conjugate gradient algorithms require significantly fewer flops for convergence than monotone “bound preserving” algorithms
Published in:
Signal Processing, IEEE Transactions on
(Volume:45
,
Issue:
3
)
Date of Publication: Mar 1997
- Page(s):
- 803 - 807
- ISSN :
- 1053-587X
- INSPEC Accession Number:
- 5531621
- Digital Object Identifier :
- 10.1109/78.558511
- Date of Current Version :
- 06 August 2002
- Issue Date :
- Mar 1997
- Sponsored by :
- IEEE Signal Processing Society
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