Linear array SAR imaging method based on iterative minimization sparse Bayesian reconstitution

Abstract

The invention provides a linear array SAR imaging method based on iterative minimization sparse Bayesian reconstitution. The method is prior distributional hypothesis based on a linear array SAR original echo signal measurement model, it is assumed that a prior probability density function of a scattering coefficient in a linear array SAR observation scene target space complies with complex exponential prior distribution, it is also assumed that a posterior probability density function of a linear array SAR original echo signal complies with Gaussian stochastic distribution, a reconstitution cost function of the scattering coefficient in the linear array SAR observation scene target space is set up through the Bayesian criterion, sparse reconstitution of the scattering coefficient in the linear array SAR observation scene target space is achieved through complex exponential distribution parametric optimization estimation and the iterative minimization constitution cost function, and therefore performance of linear array SAR sparse imaging is improved. The linear array SAR imaging method can be applied to the synthetic aperture radar imaging field, the global remote sensing field and other fields.

Description

Technical field: [0001] The technical invention belongs to the technical field of radar, and in particular relates to the technical field of synthetic aperture radar (SAR) imaging. Background technique: [0002] Compared with traditional two-dimensional synthetic aperture radar (SAR), three-dimensional synthetic aperture radar (three-dimensional SAR) can obtain three-dimensional imaging of objects in the observation scene, and can more precisely describe the geometric and scattering characteristics of the objects in the observation scene, which is more conducive to the extraction and recognition of target features , is an inevitable trend in the future development of SAR imaging technology and a current research hotspot. The basic principle of 3D SAR imaging is to synthesize a large virtual two-dimensional area array antenna through the movement of single or multiple antennas, so as to obtain two-dimensional high resolution in the plane of the area array, and then combine pu...

AI Technical Summary

Problems solved by technology

However, the BCS algorithm is based on the assumption that the sparse measurement signal obeys a Gaussian random distribution, and there are many parameters to be determined in the algorithm. It is difficult to choose a reasonable number of BCS parameters when the line array SAR sparse imaging is performed, and the reconstruction accuracy decreases when the parameter selection is improper.

Images