This article focuses on an important piece of work of the world renowned Indian statistician, Calyampudi Radhakrishna Rao. In 1945, C. R. Rao (25 years old then) published a pathbreaking paper [43], which had a profound impact on subsequent statistical research. Roughly speaking, Rao obtained a lower bound to the variance of an estimator. The importance of this work can be gauged, for instance,...

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 ...

Sparse linear arrays, such as co-prime arrays and nested arrays, have the attractive capability of providing enhanced degrees of freedom. By exploiting the coarray structure, an augmented sample covariance matrix can be constructed and MUSIC (MUtiple SIgnal Classification) can be applied to identify more sources than the number of sensors. While such a MUSIC algorithm works quite well, its perf...

The Fisher information matrix determines how much information a measurement brings about the parameters that index the underlying probability distribution for the measurement. In this paper we assume that the parameters structure the mean value vector in a multivariate normal distribution. The Fisher matrix is. then a Gramian constructed from the sensitivity vectors that characterize the first-...

The Cramer-Rao error bound provides a fundamental limit on the expected performance of a statistical estimator. The error bound depends on the general properties of the system, but not on the specific properties of the estimator or the solution. The Cramer-Rao error bound has been applied to scalarand vector-valued estimators and recently to parametric shape estimators. However, nonparametric, ...

In this paper, we derive the Cramér-Rao bound (CRB) for range estimation, which does not only exploit the range information in the time delay, but also in the amplitude of the received signal. This new bound is lower than the conventional CRB that only makes use of the range information in the time delay. We investigate the new bound in an additive white Gaussian noise (AWGN) channel with atten...

| The paper studies the Cram er-Rao (CR) bound for data obtained in emission tomography (ET). In ET the distribution of the data is the combined probability of independent Poisson distributed variables, the expectation of each being a linear function c T i of the vector of parameters. We investigate the achievability of the CR bound, in particular on the boundary of the natural domain of the pr...

The Cramér-Rao bound for estimation of parameters in continuous-time ARX models from irregularly sampled data is computed. In the derivation, the Slepian-Bangs formula is used together with a state space framework, resulting in a closed form expression for the Cramér-Rao bound. Copyright c ©2005 IFAC