Bayesian iterative reweighted sparse autofocus array SAR imaging method

Abstract

The invention discloses a Bayesian iterative reweighted sparse autofocus array synthetic aperture radar (SAR) imaging method. The Bayesian iterative reweighted sparse autofocus array SAR imaging method aims at the influence of phase errors existing in array SAR echo signals on imaging results. Based on a traditional sparse autofocus Bayesian recovery via iterative minimum (SAFBRIM) algorithm, a linear measurement matrix of scattering coefficients in the array SAR original echo signals and an observation scene target space is established, norm items in a cost function in the algorithm are subjected to iterative adaptive reweighted processing, pulse compression and equidistant surface division are carried out on a distance direction, and then each equidistant two-dimensional surface is estimated. According to the Bayesian iterative reweighted sparse autofocus array synthetic aperture radar (SAR) imaging method, different weighting coefficients are given to each norm item, and then imagesare reconstructed, so that higher-quality array SAR imaging results can be obtained. The Bayesian iterative reweighted sparse autofocus array synthetic aperture radar (SAR) imaging method has the advantages of high reconstruction precision and effective reduction of phase errors, and can be applied to the fields such as array synthetic aperture radar imaging.

Description

technical field [0001] The invention belongs to the technical field of radar, in particular to the technical field of synthetic aperture radar (SAR) imaging. Background technique [0002] As an active radar operating in the microwave band, Synthetic Aperture Radar (SAR) has all-weather and all-weather imaging capabilities, that is, it can image anytime, anywhere, whether it is day or night, sunny or thunderstorm. , to overcome the shortcomings of optical and infrared systems that cannot be imaged at night and in complex weather conditions. Traditional SAR imaging generally only has two-dimensional imaging resolution. In places with relatively large ups and downs, such as steep peaks, canyons, and tall buildings in cities, the distortion (shadow occlusion effect, spatial blur, roof, etc.) of traditional SAR imaging exists. Bottom inversion, etc.) lead to the loss of some important information in space (such as height), so it is very necessary to be able to perform three-dime...

AI Technical Summary

Problems solved by technology

However, most current CS sparse imaging algorithms rarely consider the effects of errors or uncertainties in the measurement matrix

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