The Computationally Intelligent ATR Architecture (CIAA) for SAR imagery will be driven by both point (scatter) and region features. I-MATH has previously developed point extraction algorithms. SAIC will suggest various region measurements, again drawing upon the current results of the MSTAR program. All of the raw features, both point and region associated, including the (x,y) hash point coordinates, will be input into the System Dynamics "e" Genetic Algorithm program, so as to produce two sets of evolved features. The first set will be tested with fuzzy logic rules (also evolved by the Genetic Algorithm) to generate an index to the Geometric Hashing. The primary index will be target pose (3D orientation) and possibly some estimates of target class. This will decisively reduce the size of the search space for the geometric hashing; hence a much smaller group of models will need to be matched. Currently, the hash matching determines which point model is closest to the point representation of the unknown live image. A number of metrics are computed, including percent of live and model points matched, live and model basis pair distances, average point mismatch distance, and roation angle dissimilarity. These metrics are applied with ad hoc thresholds and rules to determine the match. For the CIAA, we would instead use a second fuzzy logic rule set, again derived by the Genetic Algorithms, to compute more efficient match decision metrics. The CIAA represents an innovative structure for both improving ATR performance and simplifying the model search strategy. Notwithstanding this innovation, its components are all proven algorithms with which we are developing other successful ATR systems. In Phase I, we will provide detailed estimates of the CIAA's performance by benchmarking it to the geometric hashing-only algorithm. In Phase II, we will extend the CIAA evaluation by comparing its performance to other SAR ATR's, to especially include the MSTAR algorithm suite.
