  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 sam- ples along continuous trajectories. Examples include
magnetic resonance imaging (MRI), radio-interferometry, mobile-robot sampling,
... 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 simulation results show that this strategy is as efficient as
independent drawings while being implementable on real acquisition systems.
