One of the challenges of automated target recognition and tracking on a two-dimensional focal plane
is the ability to resolve closely spaced objects (CSO). To date, one of the best CSO-resolution algorithms
first subdivides a cluster of image pixels into equally spaced grid points; then it conjectures that K targets
are located at the centers of those sub-pixels and, for each set of such locations, calculates the associated
irradiance values that minimizes the sum of squares of the residuals. The set of target locations
that leads to the minimal residual becomes the initial starting point to a non-linear least-squares fit (e.g.
Levenberg-Marquardt, Nelder-Mead, trust-region, expectation-maximization, etc.), which completes the
estimation. The overall time complexity is exponential in K. Although numerous strides have been
made over the years vis-`a-vis heuristic optimization techniques, the CSO resolution problem remains
largely intractable, due to its combinatoric nature. We propose a novel approach to address this computational
obstacle, employing a technique that maps the CSO resolution algorithm to a quantum annealing
model which can then be programmed on an adiabatic quantum optimization device, e.g., the D-Wave
architecture.
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