Raytheon Company

Matthew D Hollenbeck

Peter Kim

Michael J Sand

A computing machine receives a real synthetic aperture radar (SAR) image including one or more targets. The real SAR image is one of a plurality of real SAR images in a training set. The computing machine generates, for the real SAR image, a model-based target shadow background (TSB) image using a three-dimensional (3D) model of the target. The computing machine generates, for the real SAR image and using an auto-encoder engine, an auto-encoder-generated TSB image using an artificial neural network (ANN). The computing machine computes, using a discriminator engine, an image difference between the auto-encoder-generated TSB image and the model-based TSB image. The computing machine adjusts weights in the auto-encoder engine based on the computed image difference.

Generative adversarial network-based target identification

A computer architecture for artificial image generation is disclosed. According to some aspects, a computing machine receives a voxel model of a first set of objects different from a target object. The target object is to be recognized using an image recognizer. The computing machine generates, based on the voxel model, a set of TSB (target shadow background-mask) images of the first set of objects. The computing machine receives, at an auto-encoder, a set of real images of the first set of objects. The computing machine generates, using the auto-encoder, one or more artificial images of the target object based on the set of TSB images. The auto-encoder learns differences between the target object and the first set of objects. The computing machine provides, as output, the generated one or more artificial images of the target object.

Growing community of inventors

Hawthorne, CA, United States of America

Matthew D Hollenbeck