公开(公告)号：US20240369697A1

公开(公告)日：2024-11-07

申请号：US18778155

申请日：2024-07-19

Applicant: The Boeing Company

Inventor： David Payton ,  Soheil Kolouri ,  Kangyu Ni ,  Qin Jiang

IPC: G01S13/90 ,  G01S7/41

Abstract: 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.

公开(公告)号：US11280899B2

公开(公告)日：2022-03-22

申请号：US16804978

申请日：2020-02-28

Applicant: THE BOEING COMPANY

Inventor： Qin Jiang ,  David Payton ,  Adour Vahe Kabakian ,  Joshua Haug ,  Brian N. Limketkai

IPC: G01S13/90 ,  G01S13/933 ,  G01S13/00

Abstract: A method of identifying a target from synthetic aperture radar (SAR) data without incurring the computational load associated with generating an SAR image. The method includes receiving SAR data collected by a radar system including RF phase history data associated with reflected RF pulses from a target in a scene, but excluding an SAR image. Range profile data is determined from the SAR data by converting the RF phase history data into a structured temporal array that can be applied as input to a classifier incorporating a recurrent neural network, such as a recurrent neural network made up of long short-term memory (LSTM) cells that are configured to recognize temporal or spatial characteristics associated with a target, and provide an identification of a target based on the recognized temporal or spatial characteristic.

公开(公告)号：US12092732B2

公开(公告)日：2024-09-17

申请号：US17450161

申请日：2021-10-06

Applicant: The Boeing Company

Inventor： David Payton ,  Soheil Kolouri ,  Kangyu Ni ,  Qin Jiang

IPC: G01S13/90 ,  G01S7/41 ,  G01S13/933

CPC classification number: G01S13/9027 ,  G01S7/417

Abstract: 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.

公开(公告)号：US20210270959A1

公开(公告)日：2021-09-02

申请号：US16804978

申请日：2020-02-28

Applicant: THE BOEING COMPANY

Inventor： Qin Jiang ,  David Payton ,  Adour Vahe Kabakian ,  Joshua Haug ,  Brian N. Limketkai

IPC: G01S13/90 ,  G01S13/933

Abstract: A method of identifying a target from synthetic aperture radar (SAR) data without incurring the computational load associated with generating an SAR image. The method includes receiving SAR data collected by a radar system including RF phase history data associated with reflected RF pulses from a target in a scene, but excluding an SAR image. Range profile data is determined from the SAR data by converting the RF phase history data into a structured temporal array that can be applied as input to a classifier incorporating a recurrent neural network, such as a recurrent neural network made up of long short-term memory (LSTM) cells that are configured to recognize temporal or spatial characteristics associated with a target, and provide an identification of a target based on the recognized temporal or spatial characteristic.