Recent studies have shown that machine learning networks trained on simulated synthetic aperture radar (SAR) images of vehicular targets do not generalize well to classification of measured imagery. This disconnect between these two domains is an interesting, yet-unsolved problem. We apply an adversarial training technique to try and provide more information to a classification network about a given target. By constructing adversarial examples against synthetic data to fool the classifier, we expect to extend the network decision boundaries to include a greater operational space. These adversarial examples, in conjunction with the original synthetic data, are jointly used to train the classifier. This technique has been shown in the literature to increase network generalization in the same domain, and our hypothesis is that this will also help to generalize to the measured domain. We present a comparison of this technique to off-the-shelf convolutional classifier methods and analyze any improvement.