Compressive sensing imaging method for synthetic aperture radar

Abstract

The invention relates to a compressive sensing imaging method for a synthetic aperture radar. The method includes: constructing a standard orthogonal basis for indicating radar echo signals by the aid of a Chirp Scaling operator, processing distance-vector and azimuth-vector two-dimensional data simultaneously by compressive sensing, and sparsely optimizing finally so as to obtain high-resolution radar images based on low data size. By the method, the data size is decreased in the distance vector and the azimuth vector firstly, then the standard orthogonal basis for indicating the radar echo signals is constructed by the aid of the Chirp Scaling operator, and two-dimensional sparse optimization is performed in the distance vector and the azimuth vector by means of compressive sensing so that the radar images are obtained. The data size can be decreased effectively under the circumstance of sparse observation scenes to lower requirements on data storage devices and transmission devices of the synthetic aperture radar, and the compressive sensing imaging method is of great practical significance to a practical system.

Description

technical field [0001] The invention relates to the field of radar signal processing, in particular to a synthetic aperture radar compression sensing imaging method combined with a Chirp Scaling algorithm. Background technique [0002] High-resolution radar often uses a wide bandwidth waveform to obtain high resolution in the range direction, and uses synthetic aperture technology to obtain high resolution in the azimuth direction. Chirp Scaling imaging algorithm (Raney, R.K., Runge, H., Bamler, R., Cumming, I.G., and Wong, F.H.: 'Precision SAR processing using chirp scaling', IEEE Transactions on Geoscience and Remote Sensing, 1994, 32, ( 4), pp.786-799) through phase multiplication to make the range migration trajectories of all targets consistent, avoiding the interpolation operation during range migration correction, and all the processing processes only need Fourier transform and phase multiplication Realization, the algorithm has high precision and fast calculation sp...

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Problems solved by technology

However, in practice, SAR often works in squint or large observation angle mode. At this time, the range migration cannot be ignored. Separate processing of the range direction and the azimuth direction requires the use of interpolation to correct the range migration, which will introduce additional computational complexity. and errors; in addition, some observation scenes have sparsity in the entire two-dimensional image domain, but a certain distance line or azimuth line does not satisfy the sparseness. At this time, the distance and azimuth are separately processed by compressive sensing, which will produce a large error

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