The Boeing Company

Kangyu Ni

David W Payton

Yuri Owechko

Qin Jiang

Soheil Kolouri

Adour Vahe Kabakian

A method for synthetic aperture radar (SAR) phase history extraction includes receiving, at a SAR system, a set of SAR phase history data derived from a plurality of return signals, the plurality of return signals produced by the SAR system illuminating a scene with a plurality of radar pulses. A region of interest (ROI) is obtained, the ROI corresponding to a moving target within the scene. A doppler shift frequency range for the moving target is determined based at least in part on an azimuth angle spread corresponding to the ROI and a known approximate trajectory of the moving target. The SAR phase history data is filtered to give extracted phase history corresponding to the moving target based at least in part on the doppler shift frequency range.

Phase history extraction for moving target

A computing system including a processor configured to train a synthetic aperture radar (SAR) classifier neural network. The SAR classifier neural network is trained at least in part by, at a SAR encoder, receiving training SAR range profiles that are tagged with respective first training labels, and, at an image encoder, receiving training two-dimensional images that are tagged with respective second training labels. Training the SAR classifier neural network further includes, at a shared encoder, computing shared latent representations based on the SAR encoder outputs and the image encoder outputs, and, at a classifier, computing respective classification labels based on the shared latent representations. Training the SAR classifier neural network further includes computing a value of a loss function based on the plurality of first training labels, the plurality of second training labels, and the plurality of classification labels and performing backpropagation based on the value of the loss function.

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Calabasas, CA, United States of America

Kangyu Ni