Recursive algorithms for computing the Cramer-Rao bound
Hero, A.O.
Usman, M.
Sauve, A.C.
Fessler, J.A.
Dept. of Electr. Eng. & Comput. Sci., Michigan Univ., Ann Arbor, MI;
This paper appears in: Signal Processing, IEEE Transactions on
Publication Date: Mar 1997
Volume: 45,
Issue: 3
On page(s): 803-807
ISSN: 1053-587X
References Cited: 11
CODEN: ITPRED
INSPEC Accession Number: 5531621
Digital Object Identifier: 10.1109/78.558511
Current Version Published: 2002-08-06
Abstract
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
Index
Terms
Available to subscribers and IEEE members.
References
Available to subscribers and IEEE members.
Citing Documents
Available to subscribers and IEEE members.
Cart
