Fast Bayesian compressed sensing method based on linear array SAR three-dimensional imaging of relevance vector machine

Abstract

The invention discloses a fast Bayesian compressed sensing (FBCS-RVM) method based on a relevance vector machine; the relevance vector machine is combined on the basis of an SBRIM algorithm, the classification of a target and a background is realized by using the relevance vector machine, and further a possible region of the target in an imaging scene space is extracted; a whole imaging scene space is replaced according to an area where a target possibly exists, a measurement matrix is simplified, the scattering coefficient of the target area is estimated, high-dimensional matrix operation in linear array SAR three-dimensional imaging is successfully avoided, and the operation efficiency of the algorithm is greatly improved; and meanwhile, the measurement matrix better represents target characteristics in an imaging scene space, the influence of false targets and sidelobe interference on high-quality imaging is better inhibited, and the imaging quality of linear array SAR three-dimensional imaging is successfully improved. The method has the advantages of high reconstruction precision and high operation efficiency, and is suitable for the fields of linear array synthetic aperture radar three-dimensional imaging and the like.

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, due to the calculation complexity of high-dimensional matrix operations (such as matrix inversion), the hardware system must have strong computing power to complete high-dimensional matrix operations

This shortcoming severely limits the application of the SBRIM algorithm in 3D sparse imaging

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