  In this work, we propose a method for determining a non-uniform sampling
scheme for multi-dimensional signals by solving a convex optimization problem
reminiscent of the sensor selection problem. The resulting sampling scheme
minimizes the sum of the Cram\'er-Rao lower bound for the parameters of
interest, given a desired number of sampling points. The proposed framework
allows for selecting an arbitrary subset of the parameters detailing the model,
as well as weighing the importance of the different parameters. Also presented
is a scheme for incorporating any imprecise a priori knowledge of the locations
of the parameters, as well as defining estimation performance bounds for the
parameters of interest. Numerical examples illustrate the efficiency of the
proposed scheme.
