A Blockwise Sparse Bayesian Learning Method for Estimating Scattering Coefficients in ISAR Imaging

A sparse Bayesian, scattering coefficient technology, applied in the field of improved block sparse Bayesian learning inverse synthetic aperture radar imaging, can solve the problem of inability to directly apply radar imaging, to improve imaging effect, eliminate fringe interference, reduce The effect of computation

Active Publication Date: 2022-04-05
SOUTHEAST UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The classic sparse Bayesian learning method does not consider this characteristic of the target. It estimates the parameters of each point of the target scattering coefficient independently, and cannot be directly applied to radar imaging.

Method used

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  • A Blockwise Sparse Bayesian Learning Method for Estimating Scattering Coefficients in ISAR Imaging
  • A Blockwise Sparse Bayesian Learning Method for Estimating Scattering Coefficients in ISAR Imaging
  • A Blockwise Sparse Bayesian Learning Method for Estimating Scattering Coefficients in ISAR Imaging

Examples

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Effect test

Embodiment 1

[0126] Example 1: This example verifies the effectiveness of the proposed block-sparse Bayesian learning ISAR imaging method, and compares the imaging results of this method with other methods under the same conditions. The methods used for comparison are range-Doppler method, sparse Bayesian learning method and matching pursuit method. The experiment uses the electromagnetic scattering simulation data of a certain type of fighter. The center frequency is 10GHz, the transmission signal bandwidth is 0.5GHz, the pitch angle is 30°, and the azimuth angle is 0°. The resolutions of the azimuth and range directions are both 0.3m, and the sweep angles are 64 points each. Imaging results compared to figure 2 shown.

Embodiment 2

[0127] Example 2: This example verifies the effectiveness of the proposed block-sparse Bayesian learning ISAR imaging method, and compares the imaging results of this method with other methods under the same conditions. The methods used for comparison are range-Doppler method, sparse Bayesian learning method and matching pursuit method. The experiment uses the electromagnetic scattering simulation data of a certain type of fighter. The center frequency is 10GHz, the transmission signal bandwidth is 0.5GHz, the pitch angle is 45°, and the azimuth angle is 45°. The resolutions of the azimuth and range directions are both 0.3m, and the sweep angles are 64 points each. Imaging results compared to image 3 shown.

[0128] Comparing the experimental results, it can be seen that compared with the range Doppler method, the image of this method is clearer without fringe interference. Compared with the sparse Bayesian learning method and the matching pursuit method, which belong to t...

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Abstract

The invention provides a method for estimating scattering coefficients of ISAR imaging by block-sparse Bayesian learning. First, the radar echo spectrum model and the two-dimensional imaging scene are discretized, and then the echo spectrum is subjected to distance-dimension pulse compression processing, and the block sparse Bayesian is used to The Yesian learning method completes the estimation of the scattering coefficient, then transposes the echo spectrum matrix, and performs azimuth dimension pulse compression processing, and also uses the block-sparse Bayesian learning method to complete the estimation of the scattering coefficient. Finally, the intersection operation is performed on the results of the two estimations to obtain the final two-dimensional ISAR imaging results. The method of the invention greatly reduces the complexity of calculation, reduces the amount of calculation, successfully eliminates the fringe interference in the image, improves the imaging effect, and improves the estimation precision of the scattering coefficient.

Description

technical field [0001] The invention belongs to the technical field of radar imaging and relates to an improved block sparse Bayesian learning inverse synthetic aperture radar (ISAR) imaging method. Background technique [0002] After years of development, compressed sensing theory has derived many sub-algorithms with good performance. Among them, the sparse Bayesian learning method has received extensive attention from researchers due to its advantages of high accuracy, fast convergence, and no need to manually adjust parameters. Different from other parameter estimation methods based on compressive sensing theory, the sparse Bayesian learning method needs to be based on the prior information of the data to be reconstructed. [0003] Even though the sparse Bayesian learning method has superior performance, there are still some problems in directly applying it to radar imaging. Usually, the scattering centers of complex targets have a clustering effect, rather than individ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01S13/90
CPCG01S13/9094G01S13/9064
Inventor 蒋忠进崔铁军陈星
Owner SOUTHEAST UNIV
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