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公开(公告)号：US20220221578A1
公开(公告)日：2022-07-14
申请号：US17643156
申请日：2021-12-07
Applicant: The Boeing Company
Inventor： Adour Vahe Kabakian , David Wayne Payton , Brian N. Limketkai , Soheil Kolouri , Qin Jiang
IPC: G01S13/90
Abstract: Described is a method for extraction of a region of interest (ROI) from a composite synthetic aperture radar (SAR) phase history data. The method comprising receiving, with a system comprising a processor, the composite SAR phase history data of a plurality of backscattered return signals produced by a SAR system illuminating a scene with a SAR beam. The method also comprises obtaining a location of a first ROI within the scene and extracting from the composite SAR phase history data a first component SAR phase history data corresponding to the ROI at the location of the ROI.
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2.发明申请公开(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.
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公开(公告)号：US12259466B2
公开(公告)日：2025-03-25
申请号：US17643156
申请日：2021-12-07
Applicant: The Boeing Company
Inventor： Adour Vahe Kabakian , David Wayne Payton , Brian N. Limketkai , Soheil Kolouri , Qin Jiang
IPC: G01S13/90
Abstract: Described is a method for extraction of a region of interest (ROI) from a composite synthetic aperture radar (SAR) phase history data. The method comprising receiving, with a system comprising a processor, the composite SAR phase history data of a plurality of backscattered return signals produced by a SAR system illuminating a scene with a SAR beam. The method also comprises obtaining a location of a first ROI within the scene and extracting from the composite SAR phase history data a first component SAR phase history data corresponding to the ROI at the location of the ROI.
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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
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.
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公开(公告)号：US11816590B2
公开(公告)日：2023-11-14
申请号：US16782480
申请日：2020-02-05
Applicant: THE BOEING COMPANY
Inventor： Shahriar Alam , Qin Jiang , Franz D. Betz , Tsai-Ching Lu , John E. Harrison , John L. Ross
Abstract: A method, system, and computer program product for predicting abnormal operation of at least one component of a machine is provided. Real time monitoring data from an operating machine is received and monitoring features that are informative of likely abnormal operation are extracted and/or calculated. The monitoring features are applied to a prediction matrix that outputs probabilities of abnormal operation within one or more prediction time horizons. If the output probabilities exceed a threshold probability, then an alert can be output. Maintenance can be automatically scheduled in response to the alert.
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Patent Agency Ranking
公开(公告)号：US10558929B2
公开(公告)日：2020-02-11
申请号：US15169233
申请日：2016-05-31
Applicant: THE BOEING COMPANY
Inventor： Shahriar Alam , Qin Jiang , Franz D. Betz , Tsai-Ching Lu , John E. Harrison , John L. Ross
Abstract: A method, system, and computer program product for predicting abnormal operation of at least one component of a machine is provided. Real time monitoring data from an operating machine is received and monitoring features that are informative of likely abnormal operation are extracted and/or calculated. The monitoring features are applied to a prediction matrix that outputs probabilities of abnormal operation within one or more prediction time horizons. If the output probabilities exceed a threshold probability, then an alert can be output. Maintenance can be automatically scheduled in response to the alert.
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7.发明申请公开(公告)号：US20190102957A1
公开(公告)日：2019-04-04
申请号：US15721533
申请日：2017-09-29
Applicant: THE BOEING COMPANY
Inventor： David J. Huber , Alex N. Waagen , Qin Jiang , Tsai-Ching Lu
Abstract: A method and apparatus for maintaining an aircraft. Real-time event information indicating faults in systems on the aircraft and aircraft condition monitoring system data indicating conditions of the systems on the aircraft are stored during a plurality of legs of flights of the aircraft. A feature table comprising the real-time event information and the aircraft condition monitoring system data is built. Feature vectors are extracted from the feature table. A machine learning algorithm is applied to the extracted feature vectors to generate a predicted maintenance event message that identifies a predicted maintenance event. The predicted maintenance event message is used to perform a maintenance operation on the aircraft.
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公开(公告)号：US20160267632A1
公开(公告)日：2016-09-15
申请号：US14657932
申请日：2015-03-13
Applicant: The Boeing Company
Inventor： Qin Jiang , Yuri Owechko
CPC classification number: G06T5/40 , G06T5/002 , G06T5/003 , G06T5/009 , G06T2207/20064 , G06T2207/20076 , G06T2207/20192
Abstract: Described herein is a method for enhancing image data that includes transforming image data from an intensity domain to a wavelet domain to produce wavelet coefficients. A first set of wavelet coefficients of the wavelet coefficients includes low-frequency wavelet coefficients. The method also includes modifying the first set of wavelet coefficients using a coefficient distribution based filter to produce a modified first set of wavelet coefficients. The method includes transforming the modified first set of wavelet coefficients from the wavelet domain to the intensity domain to produce enhanced image data.
Abstract translation: 这里描述了一种用于增强图像数据的方法，其包括将图像数据从强度域变换为小波域以产生小波系数。 小波系数的第一组小波系数包括低频小波系数。 该方法还包括使用基于系数分布的滤波器来修改第一组小波系数，以产生经修改的第一组小波系数。 该方法包括将经修改的第一组小波系数从小波域变换到强度域，以产生增强的图像数据。
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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.
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公开(公告)号：US20220229173A1
公开(公告)日：2022-07-21
申请号：US17456345
申请日：2021-11-23
Applicant: The Boeing Company
Inventor： Qin Jiang , David Wayne Payton , Soheil Kolouri , Adour Vahe Kabakian , Brian N. Limketkai
IPC: G01S13/90
Abstract: Disclosed is a synthetic aperture radar (SAR) system for target recognition with complex range profile. The SAR system comprising a memory, a recurrent neural network (RNN), a multi-layer linear network in signal communication the RNN, and a machine-readable medium on the memory. The machine-readable medium is configured to store instructions that, when executed by the RNN, cause the SAR system to perform various operations. The various operation comprise: receiving raw SAR data associated with observed views of a scene, wherein the raw SAR data comprises information captured via the SAR system; radio frequency (RF) preprocessing the received raw SAR data to produce a processed raw SAR data; converting the processed raw SAR data to a complex SAR range profile data; processing the complex SAR range profile data with the RNN having RNN states; and mapping the RNN states to a target class with the multi-layer linear network.
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