  Compressed sensing theory indicates that selecting a few measurements
independently at random is a near optimal strategy to sense sparse or
compressible signals. This is infeasible in practice for many acquisition
devices that acquire samples along continuous trajectories (e.g., radial,
spiral, ...). Examples include magnetic resonance imaging (MRI) or
radiointerferometry. In this paper, we propose to generate continuous sampling
trajectories by drawing a small set of measurements independently and joining
them using a travelling salesman problem solver. Our contribution lies in the
theoretical derivation of the appropriate probability density of the initial
drawings. Preliminary computational results show that this strategy is as
efficient as independent drawings while being implementable on real acquisition
systems.
