Multistatic radar imaging from sparse measurements

We consider the problem of multistatic radar imaging in the case when the sources and receivers are sparse and irregularly spaced. The key to handling sparse sources and receivers is the development of a data-weighting scheme that compensates for nonuniform sampling. To determine the appropriate weights, we formulate a criterion for measuring the optimality of the point-spread function, and solve the resulting optimization problem using regularized least-squares. Once the weights are determined, they can be used to compute the point-spread function and thus determine resolution, and they can also be applied to the measured data to form an image. Tests of our minimization scheme with different regularization parameters show that, with appropriate weighting, individual scatterers can be resolved at sub-wavelength scales even when the data are noisy and the locations of both sources and receivers are uncertain. The example shows that the weights determined by our method improve the resolution relative to reconstructions with constant weights.