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Analytical derivation of distortion constraints and their verification in a learning vector quantization-based target recognition system

机译：基于学习向量量化的目标识别系统中畸变约束的解析推导及其验证

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摘要

We obtain a novel analytical derivation for distortion-related constraints in a neural network- (NN)-based automatic target recognition (ATR) system. We obtain two types of constraints for a realistic ATR system implementation involving 4-f correlator architecture. The first constraint determines the relative size between the input objects and input correlation filters. The second constraint dictates the limits on amount of rotation, translation, and scale of input objects for system implementation. We exploit these constraints in recognition of targets varying in rotation, translation, scale, occlusion, and the combination of all of these distortions using a learning vector quantization (LVQ) NN. We present the simulation verification of the constraints using both the grayscale images and Defense Advanced Research Projects Agency's (DAR-PA's) Moving and Stationary Target Recognition (MSTAR) synthetic aperture radar (SAR) images with different depression and pose angles.
机译：我们获得了基于神经网络（NN）的自动目标识别（ATR）系统中失真相关约束的新解析推导。我们获得了两种类型的约束，用于涉及 4-f 相关器架构的实际 ATR 系统实现。第一个约束确定输入对象和输入相关筛选器之间的相对大小。第二个约束规定了系统实现的输入对象的旋转量、平移量和缩放量的限制。我们利用这些约束来识别旋转、平移、缩放、遮挡以及使用学习向量量化 （LVQ） NN 的所有这些失真的组合。我们展示了使用灰度图像和国防高级研究计划局（DAR-PA）的移动和静止目标识别（MSTAR）合成孔径雷达（SAR）图像对不同俯角和姿态角的约束进行仿真验证。

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