SAR non-sparse scene imaging method based on mixed sparse representation

Abstract

The invention provides a non-sparse scene SAR imaging method based on mixed sparse representation. The method includes the following steps of: 1, respectively carrying out random down-sampling in a range direction and an azimuth direction on SAR echoes of a non-sparse scene; 2, constructing a linear frequency modulation scaling imaging operator and an inverse imaging operator, and constructing a two-dimensional compressed sensing optimization model based on the approximate observation model; and 3, solving the constructed optimization model, firstly initializing the non-sparse SAR scene, performing point, line and surface decomposition on the scene, then performing curvelet transformation on a line component, performing wavelet transformation on a surface component, and then sequentially reconstructing the point, line and surface components through an iterative threshold algorithm until the algorithm converges; and finally, combining the obtained point, line and surface components to obtain a final reconstructed SAR image. According to the method, SAR imaging is carried out on a non-sparse scene containing complex features under the condition of the down-sampling rate, the data volume during imaging of the non-sparse scene can be reduced, and meanwhile, the features of the image can be enhanced.

Description

technical field [0001] The invention relates to signal and information processing technology, in particular to a SAR non-sparse scene imaging method based on hybrid sparse representation. Background technique [0002] Synthetic Aperture Radar (SAR) is an active, all-time, all-weather, high-resolution earth observation radar system that is widely used. The Nyquist sampling law limits the improvement of the resolution of the range image and the increase of the range width, because it will increase the data volume and the burden of the radar system. The Compressed Sensing (CS) theory proposed in recent years can break through the Nyquist sampling law and is superior to sparse signal reconstruction. The basic point of this theory is that a small amount of observation data can be used to reconstruct the original sparse signal with high probability A signal with a transform domain that is sparse. [0003] The existing compressed sensing SAR imaging technology is mainly to direct...

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Problems solved by technology

However, this method cannot be directly used for large-scene SAR imaging. First, the reconstruction of the phase is not considered. Second, this method is a one-dimensional compressed sensing reconstruction. The increase of the scene leads to a sharp increase in the dimension of the perception matrix, and the calculation and storage consumption follow. Increase

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