University of Electronic Science and Technology of China

Minfeng Xing

Jiang Qian

Junzheng Jiang

Haitao Lyu

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.

Noise suppression method and system for inverse synthetic aperture radar micro-cluster objects using generative adversarial network

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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Chengdu, China

Minfeng Xing