Analytical and experimental performance-complexity tradeoffs in ATR

Many automatic target recognition systems are designed based on training data. In model-based approaches, parameters are estimated from the training data and used in the actual implementation of the system. Often for a fixed-size training set, as the complexity of the model increases, the performance gets better initially then worsens. While this phenomenon is well-known in the statistics community, its importance in the design of target recognition systems is often neglected. For target recognition systems with decisions based on likelihood ratios using estimated parameters, we present complementary analytical and experimental results on this phenomenon. Analytical results assume independent samples for training and assume the existence of an underlying true distribution on the data that is not known. For several model classes, an optimal model complexity can be derived. Experimentally, these results are used to guide the design of target recognition systems for synthetic aperture radar data collected in the MSTAR program using probability of error for performance.