Radar association imaging method based on generalized total variation regularization

Abstract

The invention belongs to the technical field of radar association imaging and discloses a radar association imaging method based on generalized total variation regularization. The method comprises steps that an associated imaging model is established; the constraint of generalized total variation regularization is added, and an optimal imaging problem based on second-order TGV regular terms is established; a corresponding Lagrangian function is constructed to obtain an optimization model, and to-be-solved sub-problems are obtained through utilizing the alternating direction multiplier method and solved iteratively. The method is advantaged in that the model is solved through dividing variables and applying the multiplier alternating direction method, so a higher-quality echo reconstructionimage can be obtained, advantages of the proposed method are verified in the simulation test process, the higher resolution can be achieved, moreover, that a relatively small error can be ensured when the noise is relatively large can be seen from the mean square error of imaging, obviously, the method is further applicable to dealing with other radar image recovery problems.

Description

technical field [0001] The invention belongs to the technical field of radar correlation imaging, in particular to a radar correlation imaging method based on generalized total variation regularization. Background technique [0002] At present, the existing technologies commonly used in the industry are as follows: Radar Coincidence Imaging (RCI) is a new staring high-resolution imaging method, which constructs a space-time uncorrelated two-dimensional random Radiation field, through correlation processing to achieve high-precision inversion of the target. Compared with traditional radar imaging technology, radar correlation imaging does not require relative motion between radar and target in terms of azimuth resolution to achieve high-resolution imaging, and shortens the imaging time. Therefore, radar correlation imaging has broad application prospects in the fields of all-weather, all-time, long-distance observation area monitoring and space target recognition. In recen...

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Problems solved by technology

[0005] (2) Using the Tikhonov regular term, since the spatial high-frequency components generally correspond to information such as edges, the obtained results will be too smooth, which will affect the resolution of adjacent targets

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