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公开(公告)号：US20230162373A1
公开(公告)日：2023-05-25
申请号：US17989453
申请日：2022-11-17
Applicant: University of Electronic Science and Technology of China , Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China
Inventor： Jiang Qian , Haitao Lyu , Junzheng Jiang , Minfeng Xing
IPC: G06T7/20
CPC classification number: G06T7/20 , G06T2207/20081
Abstract: A moving target focusing method and system based on a generative adversarial network are provided. The method includes: generating, using a Range Doppler algorithm, a two-dimensional image including at least one defocused moving target, as a training sample; generating at least one ideal Gaussian point in a position of at least one center of the at least one defocused moving target in the two-dimensional image, to generate a training label; constructing the generative adversarial network, the generative adversarial network includes a generative network and a discrimination network; inputting the training sample and the training label into the generative adversarial network to perform repeated training until an output of the generative network reaches a preset condition, to thereby obtain a trained network model; and inputting a testing sample into the trained network model, to output a moving target focused image.
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Patent Agency Ranking
公开(公告)号：US12051211B2
公开(公告)日：2024-07-30
申请号：US17989453
申请日：2022-11-17
Applicant: University of Electronic Science and Technology of China , Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China
Inventor： Jiang Qian , Haitao Lyu , Junzheng Jiang , Minfeng Xing
CPC classification number: G06T7/20 , G06T2207/20081
Abstract: A moving target focusing method and system based on a generative adversarial network are provided. The method includes: generating, using a Range Doppler algorithm, a two-dimensional image including at least one defocused moving target, as a training sample; generating at least one ideal Gaussian point in a position of at least one center of the at least one defocused moving target in the two-dimensional image, to generate a training label; constructing the generative adversarial network, the generative adversarial network includes a generative network and a discrimination network; inputting the training sample and the training label into the generative adversarial network to perform repeated training until an output of the generative network reaches a preset condition, to thereby obtain a trained network model; and inputting a testing sample into the trained network model, to output a moving target focused image.
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公开(公告)号：US20250022107A1
公开(公告)日：2025-01-16
申请号：US18647290
申请日：2024-04-26
Inventor： Jiang Qian , Xin Ye , Haitao Lyu , Xiaobo Yang
Abstract: Provided is a fringe line detection and phase unwrapping method and system based on an FL-Net convolutional neural network. The method includes: constructing the FL-Net convolutional neural network; detecting fringe lines of an input image by using the FL-Net convolutional neural network to obtain an image with detected fringe lines; performing circulation integral on the detected fringe lines to repair the detected fringe lines to thereby obtain an image with repaired fringe lines; performing path integral on the repaired fringe lines to unwrap the repaired fringe lines to thereby obtain an image with unwrapped fringe lines; and identifying error points of the unwrapped fringe lines by using the FL-Net convolutional neural network; and processing the error points. Specifically, an HDC method is used to replace a down-sampling method to avoid the loss of resolution, and residual connection is used to prevent the network from being too deep.
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公开(公告)号：US12174292B2
公开(公告)日：2024-12-24
申请号：US17989162
申请日：2022-11-17
Applicant: Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China
Inventor： Haitao Lyu , Jiang Qian , Junzheng Jiang , Minfeng Xing
Abstract: A noise suppression method and system for Inverse Synthetic Aperture Radar micro-cluster objects using a generative adversarial network (GAN) are provided. The method includes: constructing the GAN, including a generator and a discriminator; obtaining and inputting noisy simulation data into the generator to obtain a first output, comparing the first output with noiseless simulation data to obtain a first generator loss, inputting the first output and the distribution function into the discriminator for denoising discrimination to obtain a first discriminant result, and determining a second generator loss according to the first generator loss and the first discriminate result; and obtaining measured data and inputting the measured data into the generator to obtain a second output, inputting the second output to the discriminator to obtain a second discriminant result, and determining a generator loss according to the second generator and the second discriminate result.
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公开(公告)号：US12181572B1
公开(公告)日：2024-12-31
申请号：US18652208
申请日：2024-05-01
Inventor： Jiang Qian , Jiayang Wu , Haitao Lyu , Guangcai Sun
Abstract: Provided are a scattering aperture imaging method and a device, a system and a storage medium. The method mainly includes four steps of scattering point position estimation, azimuth resampling, range compensation and synthetic aperture radar imaging. A phased array radar with a fixed position is used for NLOS imaging, and the radar can control a beam to scan in space, which is equivalent to a scattering aperture moving along a relay surface. Therefore, the method can realize converting NLOS imaging into LOS synthetic aperture radar imaging, which can be suitable for the situation that a relay surface is rough and the relay surface with more complicated surface condition, thus widening the application range of radar NLOS imaging.
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公开(公告)号：US20230152444A1
公开(公告)日：2023-05-18
申请号：US17989162
申请日：2022-11-17
Applicant: Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China
Inventor： Haitao Lyu , Jiang Qian , Junzheng Jiang , Minfeng Xing
CPC classification number: G01S13/9027 , G01S7/417 , G01S13/9064
Abstract: A noise suppression method and system for Inverse Synthetic Aperture Radar micro-cluster objects using a generative adversarial network (GAN) are provided. The method includes: constructing the GAN, including a generator and a discriminator; obtaining and inputting noisy simulation data into the generator to obtain a first output, comparing the first output with noiseless simulation data to obtain a first generator loss, inputting the first output and the distribution function into the discriminator for denoising discrimination to obtain a first discriminant result, and determining a second generator loss according to the first generator loss and the first discriminate result; and obtaining measured data and inputting the measured data into the generator to obtain a second output, inputting the second output to the discriminator to obtain a second discriminant result, and determining a generator loss according to the second generator and the second discriminate result.
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