1. A computer-implemented method for predicting automatic target recognition performance, the method comprising: generating a target correlation matrix each having a target correlation and a synthetic aperture radar observation space for multiple two-class combinations of target types; generating a target probability density of a target radar cross-section signature and a background probability density of a background radar cross-section signature; partitioning the observation space of each of the two-class combinations of target types into a target partition and at least one background partition in accordance with the target correlation; calculating a conditional log likelihood for each of the partitions using at least one random number in accordance with the target probability density and the background probability density; summing the conditional log likelihoods of the partitions according to each of the two-class combinations of target types associated with the correlation matrix and observation space; calculating a maximum log likelihood from the summed conditional log likelihoods given that one target type of the multiple two-class combinations of target types is assumed to be true; generating an automatic target recognition performance prediction based on the maximum log likelihood; and outputting the performance prediction via an output device.