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Bayesian compressive sensing imaging method based on generalized Pareto distribution

A technology of Bayesian compression and imaging method, which is applied in the direction of radio wave reflection/re-radiation, utilization of re-radiation, measurement devices, etc. It can solve the problems that the reconstruction performance of Bayesian compressed sensing method cannot be guaranteed, and achieve small weight Constructive errors, the effect of fewer measurements

Active Publication Date: 2018-03-30
HOHAI UNIV
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Problems solved by technology

However, the sparsity of these distributions is difficult to prove theoretically, and thus the reconstruction performance of Bayesian compressive sensing methods based on these distributions cannot be guaranteed.

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  • Bayesian compressive sensing imaging method based on generalized Pareto distribution
  • Bayesian compressive sensing imaging method based on generalized Pareto distribution
  • Bayesian compressive sensing imaging method based on generalized Pareto distribution

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[0026] In order to further describe the technical features and effects of the invention, the invention will be further described below in conjunction with the drawings and specific embodiments.

[0027] refer to Figure 1-Figure 2 , the Bayesian compressed sensing imaging method based on the generalized Pareto distribution of the present invention is used in the imaging of the inverse synthetic aperture radar measured data of the Yark-42 aircraft, the steps are as follows:

[0028] (1) Take 256 radar echoes continuously in the direction dimension, and perform motion compensation and range compression processing on the obtained data (the data has been subjected to chirp processing), to obtain 256 images consisting of 256 range units (which can be distinguished in the distance dimension The distance image composed of the smallest unit).

[0029] (2) Perform compressive sensing imaging on the signal y in the first range unit. The measurement matrix Ψ uses a Gaussian random matr...

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Abstract

The invention discloses a Bayesian compressive sensing imaging method based on generalized Pareto distribution, thereby improving performance of an existing inverse synthetic aperture radar. Accordingto the method, in inverse synthetic aperture radar imaging based on Bayesian compressive sensing, and the generalized Pareto distribution is used as prior information, thereby improving reconstruction performance of an algorithm. Compared with prior art, the Bayesian compressive sensing imaging method is advantageous in that signal reconstruction can be performed through smaller number of measurement values or on the condition of higher sparsity. An image with higher imaging quality can be obtained through simulated and actual measured inverse synthetic aperture radar data.

Description

technical field [0001] The invention belongs to the technical field of inverse synthetic aperture radar imaging, in particular to a Bayesian compressed sensing imaging method. Background technique [0002] In inverse synthetic aperture radar, because the number of strong scattering points of the target is very small, which is in good agreement with the sparsity requirement of compressed sensing, so the application of compressed sensing to inverse synthetic aperture radar imaging has great potential. Existing compressed sensing reconstruction algorithms can be directly used for inverse SAR imaging. The compressed sensing inverse SAR imaging problem can also be considered from a Bayesian point of view. The Bayesian framework offers many advantages, such as providing a measure of the certainty of the reconstructed signal, facilitating the design of adaptive measures, etc. [0003] Currently in Bayesian compressed sensing, Laplace distribution and generalized Gaussian distribu...

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Application Information

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IPC IPC(8): G01S13/90
CPCG01S13/904G01S13/9064
Inventor 成萍赵家群周晓锋
Owner HOHAI UNIV
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