The goal of this paper is to characterize the best achievable performance for the problem of estimating an unknown parameter having a sparse representation. Specifically, we consider the setting in which a sparsely representable deterministic parameter vector is to be estimated from measurements corrupted by Gaussian noise, and derive a lower bound on the mean-squared error (MSE) achievable in ...

R m p x p y p f p Cramer 1 (1) ABSTRACT One of the major tasks in the evaluation process of remotely sensed data is the determination of the exterior orientation to relate the recorded data to a geodetic reference coordinate frame. This process is called georeferencing and is very close to the concept of inverse photogrammetry realized indirect via the use of ground control. With the availabili...

Certain blind channel estimation techniques allow the identification of the channel up to a scale or phase factor. This results in singularity of the Fisher Information Matrix (FIM). The Cramér–Rao Bound, which is the inverse of the FIM, is then not defined. To regularize the estimation problem, one can impose constraints on the parameters. In general, many sets of constraints are possible but ...

summary While all nonsequential unbiased estimators of the normal mean have variances which must obey the Cramer-Rao inequality, it is shown that some sequential unbiased estimators do not.