Model constructing method of sparse microwave imaging processing

Abstract

The invention discloses a model constructing method of sparse microwave imaging processing. The model constructing method comprises the following steps of: firstly, establishing a linear regression model of microwave imaging by using a linear operator characteristic of microwave imaging according to a radar system parameter and a geometrical relationship of a radar and a scene; secondly, establishing a linear regression model of sparse microwave imaging though construction of a sparse observation model and a sparse transformation matrix; and finally, establishing a reconstruction model of sparse microwave imaging by applying a sparse signal reconstruction theory in sparse signal processing. The model constructing method of the sparse microwave imaging processing provided by the invention provides a theoretical basis for the sparse microwave imaging.

Description

technical field [0001] The invention relates to the technical field of microwave imaging, and relates to a model building method for sparse microwave imaging processing. Background technique [0002] Compared with optical imaging technology, microwave imaging is not limited by sunlight and weather conditions, and has the ability to observe targets or scenes all-weather and all-weather. It has developed into an important means of remote sensing applications such as resource exploration, environmental monitoring, and disaster assessment. [0003] Application requirements such as high-precision surveying and mapping and resource investigation, natural disasters and environmental monitoring, reconnaissance and early warning make high-resolution, wide surveying and mapping, and multi-dimensional microwave imaging technology the development direction, and the existing microwave imaging system is based on Nyquist sampling Theorem and classical digital signal processing theory, ther...

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Problems solved by technology

Third, the sparse observation matrix of microwave imaging cannot be selected arbitrarily, it is restricted by radar parameters and imaging geometry, so it is necessary to reasonably design a feasible observation matrix and data acquisition method

Fourth, in order to meet the requirements of high-resolution and wide-swath microwave imaging for earth observation, the amount of data may reach several to tens of GB even after sparse sampling, and the existing numerical optimization algorithm is still difficult to handle such a huge amount of data amount, and there is a problem of low efficiency, so it is necessary to realize the robustness and rapidity of the reconstruction optimization algorithm

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