Blocked sparse Bayesian learning ISAR imaging scattering coefficient estimation method

A technology of sparse Bayesian and Bayesian learning, applied in the field of improved block sparse Bayesian learning inverse synthetic aperture radar imaging, which can solve problems such as radar imaging that cannot be directly applied

Active Publication Date: 2019-08-16
SOUTHEAST UNIV
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  • Application Information

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Problems solved by technology

The classic sparse Bayesian learning method does not consider this characteristic of the target. It estimates the param

Method used

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

Examples

Experimental program
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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 blocked sparse Bayesian learning ISAR imaging scattering coefficient estimation method. The method comprises the following steps: firstly discretizing a radar echo spectrum model and a two-dimensional imaging scene, and then performing distance-dimensional pulse compression processing on the echo spectrum; accomplishing scattering coefficient estimation by utilizing the block sparse Bayesian learning method, performing transition on an echo spectrum matrix, and performing orientation-dimensional pulse compression processing, and accomplishing the scattering coefficientestimation by utilizing the block sparse Bayesian learning method; and finally, taking intersection of two estimation results to operate, and obtaining a final two-dimensional ISAR imaging result. Through the method provided by the invention, the operation complexity is greatly reduced, the operand is reduced, the fringe interference in the image is successfully eliminated, an imaging effect is improved, and the scattering coefficient estimation precision is improved.

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