Co-prime array decomposition and fusion-based three-dimensional SAR sparse imaging method

Abstract

The invention discloses a co-prime array decomposition and fusion-based three-dimensional SAR sparse imaging method; the method comprises the following steps of firstly, performing a compressed sensing iteration minimization sparse bayesian reconstruction (SBRIM) imaging algorithm on a co-prime array and a sub-array of the co-prime array for imaging separately to obtain three synthetic aperture radar images; and then, fusing the obtained imaging results to obtain a final array decomposition and fusion-based three-dimensional SAR sparse imaging result. According to the method, the advantages ofa co-prime sampling method and a compressed sensing sparse reconstruction method are combined; the sampling data can be reduced by utilizing the co-prime sampling; compared with a traditional randomsampling mode, the method has the advantages that the system is easier to realize; and compared with a traditional sparse imaging method, the method disclosed by the invention can effectively inhibitgrating lobes and false targets and improve the 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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