Multi-band multi-view ISAR fusion imaging method

Abstract

The invention discloses a multi-band multi-view ISAR fusion imaging method, and relates to the technical field of image processing methods. According to the method, firstly, a vectorization sparse representation model of multi-band multi-view ISAR fusion imaging is established, and in order to reduce the calculation complexity during solving and improve the imaging efficiency, on the basis of a Bregman iteration algorithm, the iteration convergence speed of the algorithm is further improved in combination with weighted residual error back substitution and basis matrix condition number optimization; a simulation experiment verifies the effectiveness and superiority of the algorithm in realizing one-dimensional sparse signal reconstruction and multi-band multi-view ISAR fusion imaging.

Description

technical field [0001] The present invention relates to the technical field of image processing methods, in particular to a multi-band multi-view ISAR fusion imaging method. Background technique [0002] Inverse Synthetic Aperture Radar (Inverse Synthetic Aperture Radar, ISAR) can perform high-resolution imaging of targets at long distances and under all-weather conditions, and has been widely used in military and civilian fields. For a single ISAR imaging system, the distance resolution and azimuth resolution of imaging are usually improved by increasing the transmission signal bandwidth and increasing the observation coherent accumulation time, but at the same time it will bring high hardware complexity, high manufacturing cost, and Problems such as difficulty in motion compensation lead to limited imaging resolution of the directly improved single radar system. Multi-band and multi-angle ISAR fusion imaging technology uses multiple radar echoes obtained from different an...

AI Technical Summary

Problems solved by technology

At this time, if a greedy algorithm is used to solve the problem, such as the OMP algorithm, the reconstruction accuracy is limited, and the sparsity of the signal needs to be predicted, which is difficult to estimate accurately in the actual process, which restricts the applicability of the algorithm; if the Bayesian inference class is used Algorithm solution, high estimation accuracy, but involves a large number of inversion steps, resulting in a large amount of calculation of the algorithm, which is not conducive to real-time processing

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