Sparse microwave imaging method based on online observation

Abstract

The invention provides a sparse microwave imaging method based on online observation. The method comprises the following steps that S1, according to the radar observation imaging principle, a sparse microwave imaging module based on the sparse approximation theory is constructed; S2, a value estimated for the nth time of a backscatter coefficient of a target scene is set to be x(n); S3, a value x(n+1) estimated for the n+1th time of the backscatter coefficient of the target scene is obtained online by means of observation based on the homotopy algorithm through the backscatter coefficient x(n) of the target scene and newly-added radar observation echo data yn+1; the steps are repeated continuously until a stopping condition is satisfied, so that sparse microwave imaging is achieved. According to the sparse microwave imaging method based on online observation, the smallest sampling number can be determined in a self-adaptive mode according to the characteristics of the scene, an original result can be directly corrected by newly-added sampling data, and therefore recalculation is avoided and the calculated amount is reduced.

Description

technical field [0001] The invention belongs to the technical field of radar imaging, and relates to an imaging processing method based on sparse approximation theory. Background technique [0002] Synthetic aperture radar (SAR) is an active observation radar that uses an aircraft as a motion platform to continuously send and receive electromagnetic waves along the flight track to perform high-resolution imaging of ground scenes. It is not affected by weather, day and night and other conditions, and has all-weather and all-weather detection capabilities. [0003] Recently, the Institute of Electronics, Chinese Academy of Sciences proposed a new system for sparse microwave imaging, and also hopes to obtain the sparse microwave signal of the observed object through sparse sampling in the space, time, frequency spectrum or polarization domain [1] by looking for the sparse representation domain of the observed object ( [1] ZHANG BingChen, HONG Wen, WU YiRong, "Sparse microwave ...

AI Technical Summary

Problems solved by technology

However, for actual situations, the degree of sparsity is often not known in advance, and the actual sampling rate is often much higher than the theoretical minimum sampling rate of the actual system to ensure system reliability.

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