Multi-Target Discrimination with Linear Signal Decomposition/Direction of Arrival Based ATR

Large computational complexity arises in model-based ATR systems because an object's image is typically a function of several degrees of freedom. Most model-based ATR systems overcome this dependency by incorporating an exhaustive library of image views. This approach, however, requires enormous storage and extensive search processing. Some ATR systems reduce the size of the library by forming composite averaged images at the expense of reducing the captured pose speciic information, usually resulting in a decrease in performance. The Linear Signal Decomposition/Direction of Arrival (LSD/DOA) system, on the other hand, forms an essential-information object model which incorporates pose speciic data into a much smaller data set, thus reducing the size of the image library with less loss of discrimination and pose estimation performance. The LSD/DOA system consists of two independent components: a computationally expensive oo-line component which forms the object model and a computationally inexpensive on-line object recognition component. The focus of this paper will be on the development of the multi-object Generalized Likelihood Ratio Test (GLRT) as applied to the LSD/DOA ATR system. Results will be presented from the testing of the LSD/DOA multi-object ATR system for SAR imagery using four targets, represented over a wide range of viewing angles.