Sparse aperture ISAR self-focusing method based on atomic norm and image entropy joint constraint

Abstract

The invention discloses a sparse aperture ISAR (Inverse Synthetic Aperture Radar) self-focusing method based on atomic norm and image entropy joint constraint, and mainly solves the problem that the initial phase error compensation precision of a traditional ISAR self-focusing algorithm is insufficient under the sparse aperture condition. The method mainly comprises the steps of 1 modeling sparseaperture ISAR echoes; 2 reconstructing an ISAR image according to an atomic norm minimization algorithm; 3 estimating an initial phase error through a minimum entropy criterion; and 4 performing iterative solution until the entropy of the ISAR image converges. According to the method, the initial phase error can be effectively estimated from the sparse aperture radar echo, ISAR self-focusing is realized, and the influence of side lobes and grating lobes introduced by undersampling can be effectively eliminated, so that the ISAR image is clearer.

Description

technical field [0001] The invention belongs to the technical field of radar signal processing, in particular to a sparse aperture ISAR self-focusing method based on joint constraints of atomic norm and image entropy. Background technique [0002] Inverse synthetic aperture radar (ISAR) can obtain high-resolution two-dimensional images of moving targets. Compared with optical equipment, ISAR has the advantages of all-day, all-weather, and strong penetration, and has become an important detection equipment for space target recognition. It has been widely used in space target surveillance, missile defense, radar astronomy and other fields. At present, for the complete radar echo signal (or full aperture signal), the traditional ISAR imaging method can already obtain high-resolution images of moving targets, but for the sparse aperture signal, the traditional method basically fails, and there is still a lack of effective imaging means. [0003] There are many reasons for the s...

AI Technical Summary

Problems solved by technology

At present, for the complete radar echo signal (or full aperture signal), the traditional ISAR imaging method can already obtain high-resolution images of moving targets, but for the sparse aperture signal, the traditional method basically fails, and there is still a lack of effective imaging means

[0003] There are many reasons for the sparse aperture in the actual ISAR imaging process: First, the radar may be in the mode of alternating wide and narrow bands. The narrow band is used to measure the position and velocity of the target, and the wide band is used for imaging. At this time, the radar cannot continuously transmit broadband signals; It is the multi-channel radar that observes and images multiple targets at the same time, and frequent channel switching also makes it impossible to obtain some echo pulses; the third is the fierce offensive and defensive confrontation in modern warfare, which causes some echo pulses to be interfered and no longer available

Sparse aperture seriously affects the performance of traditional ISAR imaging methods. On the one hand, non-uniform sampling causes the traditional fast Fourier transform (FFT)-based range-Doppler (RD) imaging method to be affected by strong side lobes and grating lobes. The resolution is reduced; on the other hand, under the condition of sparse aperture, the coherence between radar echo pulses is destroyed, and the traditional self-focusing algorithm based on prominent points basically fails, while the self-focusing algorithm based on image statistical characteristics, such as the minimum Although the entropy (Minimum Entropy Algorithm, ME) self-focusing algorithm is less affected, the compensation accuracy of the initial phase error is difficult to meet the requirements under the condition of low aperture sparsity

If the initial phase error caused by target translation cannot be effectively compensated, the ISAR image will be defocused

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