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.

Date Published: 2 February 2014
PDF: 5 pages
Proc. SPIE 9020, Computational Imaging XII, 90200D (2 February 2014); doi: 10.1117/12.2042604

Published in SPIE Proceedings Vol. 9020:
Computational Imaging XII
Charles A. Bouman; Ken D. Sauer, Editor(s)