Fractal dimension threshold iteration sparse microwave imaging method based on sparseness estimation

Abstract

The invention provides a fractal dimension threshold iteration sparse microwave imaging method based on sparseness estimation. The method comprises the following specific steps: step 1, a radar observation model is established by using radar system parameters tested actually, and the sparseness estimation value of a target scene is calculated through the radar observation model; step 2, according to the sparseness estimation value, thresholds corresponding to striped areas of different orthogonal dimension coordinates are set self-adaptively, and the coordinate position of a strong point target is evaluated by using a fractal dimension threshold iteration method; step 3, based on the coordinate position of the strong point target, the back scattering coefficients of the target scene is recovered by using a least squares supported projection method to achieve microwave imaging. The method can obtain radar echo data under the downsampling condition, and high-quality and high-precision microwave images are reconstructed.

Description

technical field [0001] The invention belongs to the technical field of radar two-dimensional imaging under the sparse microwave imaging system, and in particular relates to a fractal dimension threshold iterative sparse microwave imaging method based on sparse degree estimation. Background technique [0002] As an active microwave imaging technology, Synthetic Aperture Radar (SAR) has many advantages such as all-time, all-weather, and high imaging resolution. The sampling data used in traditional SAR imaging technology is obtained by sampling according to the Nyquist sampling law. With the continuous improvement of the imaging quality requirements of the SAR system, the sampling process based on Nyquist sampling law will inevitably generate a large amount of sampled data, making the real-time storage, real-time transmission and real-time processing of the sampled data more and more difficult. However, many target scenes observed by radar systems are sparse on a specific bas...

AI Technical Summary

Problems solved by technology

[0003] The technical problem to be solved by the present invention is: when the sampling rate is greatly reduced, there will be a large number of false targets in the imaging results obtained by the overall threshold iterative sparse microwave imaging method. For the resulting target scene backscatter coefficient estimation The value cannot truly reflect the electromagnetic scattering characteristics of the target scene, and a sparse microwave imaging method based on dimensional threshold value iterative sparseness estimation is proposed.

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