Multi-base ISAR fusion imaging method based on variational Bayesian learning

Abstract

The invention discloses a multi-base ISAR fusion imaging method based on a variational Bayesian learning algorithm. The multi-base ISAR fusion imaging method mainly solves the problem that estimationof multi-base ISAR image fusion and target rotation parameter and radar observation visual angle difference is complex under the existing low signal-to-noise ratio condition. The multi-base ISAR fusion imaging method comprises the steps of: 1) receiving echoes of two radars and carrying out range pulse compression; 2) carrying out vectorization operation on the echoes after range direction pulse compression and splicing the echoes to obtain an observation vector; 3) constructing dictionary matrixes of the two radars; 4) constructing a fusion imaging model according to the observation vector and the dictionary matrixes of the two radars; 5) estimating a target rotation angle and a second radar observation visual angle; 6) and solving a scattering coefficient vector after fusion in the fusion imaging model obtain a fusion imaging result. The multi-base ISAR fusion imaging method based variational Bayesian learning is simple in estimation, can obtain the optimal estimation of the target rotation parameter and the radar observation visual angle difference, achieves the high-resolution fusion imaging of the target under a low signal-to-noise ratio, and can be used for the extraction andidentification of the shape features of the target.

Description

technical field [0001] The invention belongs to the technical field of radar, and further relates to a multi-base ISAR high-resolution fusion imaging method, which can be used for object shape feature extraction and recognition. Background technique [0002] With the rapid development of inverse synthetic aperture radar (ISAR), although existing imaging radars can provide higher resolution, it is necessary to obtain higher resolution images when observing space objects such as space debris, small satellites, and spacecraft. 2D radar imagery to accurately characterize it. Existing studies have shown that high-resolution images of targets observed from a wide range of perspectives can effectively improve the reliability of target recognition, while fusing target observation data from different perspectives can improve the resolution of imaging results, laying the foundation for high-performance target recognition. Generally speaking, there are two ways to obtain large-scale m...

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Problems solved by technology

However, this method needs to extract the position of the scattering point, which will affect the final imaging result in the presence of unfavorable conditions such as noise.

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