Method for imaging and identification integration based on compressed sensing and target prior information

Abstract

The invention belongs to the field of compressed sensing reconstruction based on target prior information and particularly relates to a method for imaging and identification integration based on compressed sensing and the target prior information. The method comprises the steps of obtaining electromagnetic scattering characteristics of targets; performing sparse imaging grid division on an imaging area and utilizing the obtained electromagnetic scattering characteristics to construct a characterization dictionary; obtaining echo data of the imaging area and working out observed data through a construction measurement matrix; utilizing the characterization dictionary in the steps and a CS algorithm to perform sparse reconstruction treatment on the observed data in the steps to obtain a sparse coefficient; enabling every element in the sparse coefficient to be in one-to-one correspondence with imaging area grids, and when the elements are nonzero values, judging that the targets exist at the corresponding positions of the imaging area grids; and if the elements are zero, judging that no target exists at the corresponding positions of the imaging area grids. Target discrimination is finished while sparse imaging on the targets is achieved, and the method is good in discrimination robustness and consistency.

Description

technical field [0001] The invention belongs to the field of compressed sensing (Compressed Sensing, CS) reconstruction processing based on target prior information, and in particular relates to an integrated imaging identification method based on compressed sensing and target prior information. Background technique [0002] CS is a new research field developed in recent years based on signal sparse representation and approximation theory. The theory points out that as long as the signal is compressible or sparse in a certain transform domain, then it can be used with a transform basis The uncorrelated observation matrix projects the transformed high-dimensional signal onto a low-dimensional space, and then by solving an optimization problem, the original signal can be reconstructed with high probability from these small projections. It can be proved that such a projection contains heavy sufficient information to construct the signal. Since the development of CS theory, it ...

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Problems solved by technology

However, according to the construction process of FD, it is not difficult to find that since DSS only contains the scattering structure information of landmines, the identification ability is not strong, and the applicability is also greatly limited.

For different BoR targets with similar DSS (such as metal cylinders, metal spheres) and BoR targets without DSS, the discrimination power of FD will be greatly reduced

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