Genetic algorithms for unconventional imaging

Unconventional imaging techniques obtain high resolution images of objects at very long ranges without the use of large diameter primary optical elements. Cost and weight constraints lead us to consider methods for using sparse arrays of subapertures. In this paper, we present a genetic algorithm method for designing sparse arrays of subapertures for an unconventional imaging technique known as correlography. We have compared the solutions found using genetic algorithms to other techniques for generating arrays with filled autocorrelations. The results of this comparison are presented in this paper.