A 3D Sar Sparse Imaging Method Based on Coprime Array Decomposition and Fusion

Abstract

The present invention discloses and proposes a three-dimensional SAR sparse imaging method based on the decomposition and fusion of coprime arrays. In the method, the coprime array and the subarrays of the coprime array are firstly reconstructed by compressive sensing iterative minimization of sparse Bayesian reconstruction. The (SBRIM) imaging algorithm performs imaging separately to obtain three synthetic aperture radar images, and then fuses the obtained imaging results to obtain the final 3D SAR sparse imaging result based on array decomposition and fusion. The present invention combines the advantages of the coprime sampling method and the compressed sensing sparse reconstruction method. The coprime sampling method can not only reduce the sampling data, but also is easier to realize systematically than the traditional random sampling method; compared with the traditional sparse imaging method, the method of the present invention can Effectively suppress grating lobes and false targets and improve imaging quality.

Description

technical field [0001] The invention belongs to the technical field of radar, in particular to the technical field of synthetic aperture radar (SAR) imaging. Background technique [0002] Synthetic Aperture Radar (SAR), as an all-day, all-weather, and information-rich remote sensing imaging technology, has become an important means of earth observation today. Precision strikes, land and resources exploration, and natural disaster monitoring have been more and more widely used in the national economy and military fields. For details, see the literature "Liu Guoxiang, Ding Xiaoli, Chen Yongqi, etc. A new technology for space earth observation with great potential-Synthetic aperture radar Interference [J]. Advances in Earth Sciences, 2000,15(6):734-740". Traditional SAR imaging generally only has two-dimensional imaging resolution. In places with relatively large ups and downs, such as steep peaks, canyons, and tall buildings in cities, the distortion (shadow occlusion effect,...

AI Technical Summary

Problems solved by technology

If the observation scene is sparse, high-resolution imaging of the target scene can be achieved with a small number of samples, but the random sampling array mode has high hardware complexity and is difficult to implement, while the coprime array uses a pair of shared first Sparse array structure composed of uniform and sparse sampling sub-arrays with array elements and sampling intervals that are relatively prime, the array is simple to implement, and the hardware is easy to implement

However, the number and position of the array elements in the array antenna determine that the cross-correlation between adjacent arrays of the compressed sensing measurement matrix increases, the signal crosstalk is serious, there are false targets in the imaging, and the imaging quality decreases.

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