  A recently identified class of receivers which demultiplex an optical field
into a set of orthogonal spatial modes prior to detection can surpass canonical
diffraction limits on spatial resolution for simple incoherent imaging tasks.
However, these mode-sorting receivers tend to exhibit high sensitivity to
contextual nuisance parameters (e.g., the centroid of a clustered or extended
object), raising questions on their viability in realistic imaging scenarios
where little or no prior information about the scene is available. We propose a
multi-stage passive imaging strategy which segments the total recording time
between different physical measurements to build up the required prior
information for near quantum-optimal imaging performance at sub-Rayleigh length
scales. We show via Monte Carlo simulations that an adaptive two-stage scheme
which dynamically allocates the total recording time between a traditional
direct detection measurement and a binary mode-sorting receiver outperforms
idealized direct detection alone for simple estimation tasks when no prior
knowledge of the object centroid is available, achieving one to two orders of
magnitude improvement in mean squared error. Our scheme can be generalized for
more sophisticated imaging tasks with multiple parameters and minimal prior
information.
