Bunching SAR compressed sensing imaging method based on approximate observation matrix

Abstract

The invention relates to a bunching SAR compressed sensing imaging method based on an approximate observation matrix. The bunching SAR compressed sensing imaging method comprises the following steps:1, obtaining a matrix form of distance-direction scale transformation based on a polar coordinate format algorithm of scale transformation; 2, obtaining a matrix form of azimuth scale transformation based on the polar coordinate format algorithm of scale transformation; 3, deriving a matrix expression form of a polar coordinate format algorithm imaging process according to results of the step 1 and the step 2; 4, deriving a signal model of an inverse imaging process according to a signal model of a radar projection matrix obtained in the step 3; and 5, constructing a CS reconstruction model byusing a PFA imaging process and an inverse imaging process obtained in the above steps and an iterative threshold shrinkage algorithm based on the approximate observation matrix. According to the method, sparse constraint is carried out on the PFA imaging process, the application range of PFA imaging in the SAR field is wider, and compared with a traditional accurate measurement matrix, the calculation complexity and the storage memory are greatly reduced.

Description

technical field [0001] The invention belongs to the field of radar technology, and in particular relates to a spotlight SAR compressed sensing imaging method based on an approximate observation matrix. Background technique [0002] Traditional synthetic aperture radar (Synthetic Aperture Radar, SAR) usually transmits and receives signals based on linear tracks, and realizes all-day and all-weather detection and imaging after coherent processing of echoes. In a SAR system, the radar transmits a series of pulses along its path and receives the echoes scattered from the target as raw data. Traditionally, the reconstruction of the scene is achieved by focusing the image based on the Polar Format Algorithm (PFA), which is efficient, but SAR imaging with increasing resolution and coverage requires more and more measurements, storage and downlink Bandwidth, however, is often hindered by current system hardware for such high-dimensional applications. [0003] In recent years, the ...

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Problems solved by technology

In the existing published literature, many experts and scholars propose a CS-based framework by sampling data in the frequency domain, but these works are actually not applicable to CS-SAR systems that wish to sample in the time domain; in addition , in most literatures, a more general CS-SAR model is proposed by precisely discretizing the SAR observation function into an observation matrix and solving it directly by CS

All these works fully demonstrate that CS-SAR does have some unique advantages compared to conventional SAR imaging methods, such as relaxation of the required measurements, reduction of sidelobes, and further suppression of noise, however, in all applications Among them, serious disadvantages have been observed: Compared with traditional methods, the computational complexity and storage cost of CS-SAR model are much higher, so it is very inefficient to apply it to large scene conditions, and related literature regards MF (matching filtering) and CS to form a new CS-SAR imaging method, which reduces the computational complexity and storage cost, but this method is based on the MF imaging process, which is relatively simple and has less application range

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