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Improved sparse Bayesian learning ISAR imaging scattering coefficient estimation method

A technique of sparse Bayesian and scattering coefficients, which is applied in the field of improved sparse Bayesian learning inverse synthetic aperture radar imaging scattering coefficient estimation, can solve problems such as unsatisfactory requirements, and achieve reduced computational complexity, clear imaging, and reduced matrix effect of scale

Active Publication Date: 2019-08-23
SOUTHEAST UNIV
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Problems solved by technology

However, the traditional sparse Bayesian learning method is still limited to the reconstruction of one-dimensional signals, and it needs to deal with huge matrices when applied to the field of radar imaging, which cannot meet the needs

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  • Improved sparse Bayesian learning ISAR imaging scattering coefficient estimation method
  • Improved sparse Bayesian learning ISAR imaging scattering coefficient estimation method
  • Improved sparse Bayesian learning ISAR imaging scattering coefficient estimation method

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Embodiment 1

[0128] Embodiment 1: This example verifies the effectiveness of the improved sparse Bayesian learning ISAR imaging method proposed by the present invention. The method proposed by the present invention is compared with the range Doppler method, the traditional sparse Bayesian learning method and the matching pursuit method, and all methods perform imaging under the same conditions. The experiment uses the electromagnetic scattering simulation data of a certain type of fighter. The center frequency is 10GHz, and the transmission signal bandwidth is 0.5GHz; the pitch angle is 30°, and the azimuth angle is 0°; Take 64 points for each corner. Imaging results compared to figure 2 shown.

Embodiment 2

[0129] Embodiment 2: This example verifies the effectiveness of the improved sparse Bayesian learning ISAR imaging method proposed by the present invention. The method proposed by the present invention is compared with the range Doppler method, the traditional sparse Bayesian learning method and the matching pursuit method, and all methods perform imaging under the same conditions. The experiment uses the electromagnetic scattering simulation data of a certain type of fighter. The center frequency is 10GHz, and the transmission signal bandwidth is 0.5GHz; the pitch angle is 45°, and the azimuth angle is 45°; Take 64 points for each corner. Imaging results compared to image 3 shown.

[0130] From the comparison of the experimental results in the two embodiments, it can be seen that compared with the range Doppler method, the strong scattering points of the present invention are more obvious, and the image is clearer. Compared with the matching pursuit method and the traditi...

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Abstract

The invention provides an improved sparse Bayesian learning ISAR imaging scattering coefficient estimation method. The method comprises the steps of: firstly discretizing an echo signal spectrum modeland a two-dimensional imaging scene, then performing distance dimension pulse compression processing on an echo signal spectrum, and establishing a sparse Bayesian learning model for the echo signalspectrum, initializing a scattering coefficient priori variance and a noise prior variance; estimating a posterior mean and a posterior variance of a scattering coefficient based on the scattering coefficient priori variance and the noise prior variance; changing the posterior mean of the scattering coefficient by using L0 norm minimization processing; reversely updating the scattering coefficientpriori variance and the noise prior variance based on the posterior mean and the posterior variance of the scattering coefficient; and performing repeated iteration to continuously optimize and update related parameters. After iteration convergence, a posterior mean matrix of the scattering coefficient is a desired imaging result. By adopting the method provided by the invention, the computational complexity is reduced, and the ISAR imaging effect is improved.

Description

technical field [0001] The invention belongs to the technical field of radar imaging, and relates to an improved sparse Bayesian learning inverse synthetic aperture radar (ISAR) imaging scattering coefficient estimation method. Background technique [0002] In recent years, compressive sensing techniques have been extensively studied and successfully applied to signal and image reconstruction. Compressed sensing theory points out that if the signal is sparse, or can be well approximated by the sparse signal (compressible), a sampling matrix unrelated to the transformation matrix can be used to project the high-dimensional signal onto the low-dimensional space. Then by solving an optimization problem with sparse constraints, the original signal can be recovered with a high probability from these small amount of projection data. Due to the sparse distribution of radar scattering centers in space, the compressed sensing method is suitable for ISAR high-resolution imaging and h...

Claims

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Application Information

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IPC IPC(8): G01S13/90
CPCG01S13/9094G01S13/9064
Inventor 蒋忠进崔铁军陈星
Owner SOUTHEAST UNIV
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