Bistatic ISAR sparse aperture imaging method

Abstract

The invention provides a bistatic ISAR sparse aperture imaging method, which relates to the technical field of radar signal processing and mainly solves the problems of low imaging resolution and longoperation time and the like of the bistatic ISAR under a sparse aperture condition. According to the realization process, a bistatic ISAR echo model is built, the echo after translation compensationis further subjected to Doppler displacement compensation, and full-aperture echo data are obtained; the echo is subjected to sparse representation, and a compressed sensing-based bistatic ISAR sparseaperture echo model is built; the whole two-dimensional echo data are blocked, and each pixel of a target image is assumed to obey Gaussian priori to build a sparse Bayesian model; a fast edge likelihood function maximization method is used to solve and obtain a high-quality target image; the target images corresponding to the solved blocks of echo are combined to a whole two-dimensional image, and a reconstructed target image is obtained. Through the technical scheme of the invention, while the reconstruction quality is ensured, the operation efficiency can be improved at the same time.

Description

technical field [0001] The invention relates to the technical field of radar signal processing, in particular to a bistatic ISAR sparse aperture imaging method. Background technique [0002] Bistatic ISAR is an ISAR imaging system in which the transmitting station and the receiving station are separated. It not only has the "four-resistance" characteristics of bistatic radar, but also has no geometric imaging blind zone and can obtain more target information compared with single-static ISAR. It has become one of the hot issues in the research of modern radar technology. [0003] In the process of target observation, it is usually necessary to switch radar beams to observe multiple targets from multiple perspectives, which is easy to form sparse apertures. If the traditional RD algorithm is directly used to realize bistatic ISAR imaging, it will not only produce strong sidelobes and energy leakage, but also have a lot of interference noise under the condition of low signal-t...

AI Technical Summary

Problems solved by technology

In the reconstruction algorithm, based on l 1 Although the norm-constrained convex optimization algorithm can effectively improve the imaging quality and anti-noise ability, it cannot perform parameter self-learning when solving the solution. It is necessary to estimate the constrained parameters first, and the estimation error has a great impact on the imaging results, and sometimes it may not be possible to obtain the best results. Excellent sparse solution, affecting image quality

In addition, the echo data vectorization and real number operations are involved in the solution, resulting in a large amount of data storage and calculation, and the calculation efficiency needs to be improved

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