22 research outputs found
Cramer-Rao Bound for Sparse Signals Fitting the Low-Rank Model with Small Number of Parameters
In this paper, we consider signals with a low-rank covariance matrix which
reside in a low-dimensional subspace and can be written in terms of a finite
(small) number of parameters. Although such signals do not necessarily have a
sparse representation in a finite basis, they possess a sparse structure which
makes it possible to recover the signal from compressed measurements. We study
the statistical performance bound for parameter estimation in the low-rank
signal model from compressed measurements. Specifically, we derive the
Cramer-Rao bound (CRB) for a generic low-rank model and we show that the number
of compressed samples needs to be larger than the number of sources for the
existence of an unbiased estimator with finite estimation variance. We further
consider the applications to direction-of-arrival (DOA) and spectral estimation
which fit into the low-rank signal model. We also investigate the effect of
compression on the CRB by considering numerical examples of the DOA estimation
scenario, and show how the CRB increases by increasing the compression or
equivalently reducing the number of compressed samples.Comment: 14 pages, 1 figure, Submitted to IEEE Signal Processing Letters on
December 201
Second-order parameter estimation
This work provides a general framework for the design of second-order blind estimators without adopting any
approximation about the observation statistics or the a priori
distribution of the parameters. The proposed solution is obtained
minimizing the estimator variance subject to some constraints on
the estimator bias. The resulting optimal estimator is found to
depend on the observation fourth-order moments that can be calculated
analytically from the known signal model. Unfortunately,
in most cases, the performance of this estimator is severely limited
by the residual bias inherent to nonlinear estimation problems.
To overcome this limitation, the second-order minimum variance
unbiased estimator is deduced from the general solution by assuming
accurate prior information on the vector of parameters.
This small-error approximation is adopted to design iterative
estimators or trackers. It is shown that the associated variance
constitutes the lower bound for the variance of any unbiased
estimator based on the sample covariance matrix.
The paper formulation is then applied to track the angle-of-arrival
(AoA) of multiple digitally-modulated sources by means of
a uniform linear array. The optimal second-order tracker is compared
with the classical maximum likelihood (ML) blind methods
that are shown to be quadratic in the observed data as well. Simulations
have confirmed that the discrete nature of the transmitted
symbols can be exploited to improve considerably the discrimination
of near sources in medium-to-high SNR scenarios.Peer Reviewe