The Adaptive SAR ATR Problem Set (AdaptSAPS) poses a typical "learning with a critic" problem, in which the system-under-test (SUT) is initially trained to characterize a subset of target objects (e.g. T72) and a subset of non-target objects (e.g. clutter), and is to be updated on-line using the Target Truth information. This work proposes an SUT for adaptive SAR imagery exploitation. The system is founded on a novel feature vector generation scheme and Linear Discriminant Analysis (LDA). The proposed feature vector generation scheme partitions SAR image chips into subimage blocks. The distribution density of subimage blocks is fitted as a Gaussian Mixture Model (GMM). Feature vector of each SAR image is composed of log-likelihoods of its subimage blocks on the pre-fitted GMM. Comparing to original SAR image chips, feature vectors generated from log-likelihoods display superior discriminative power. After feature generation, LDA is used to project feature vectors into a 1-dimensional subspace for classification. The performance of the proposed system is evaluated on the AdaptSAPS.