A novel sparse synthetic aperture radar unambiguous imaging method based on mixed-norm optimization
Compared with traditional matched filtering (MF) based methods, sparse synthetic aperture radar (SAR) imaging could obtain high-quality images of sparse surveillance regions from down-sampled echo data. However, sparse SAR ...
The received echo is discrete and finite in the time domain and shows infinite discreteness in the range-Doppler domain, which is equivalent to the periodic extension of the frequency spectrum of the main frequency band signal. The azimuth ambiguity is not serious when the pulse repetition frequency (PRF) satisfies the Shannon-Nyquist sampling theory. However, the sparse SAR system always aims to obtain a wide swath by reducing PRF, which will cause azimuth ambiguity, and even lead to failed reconstruction.
To solve the above problems, in a paper published in the journal Science China Information Sciences, with the help of approximated observation theory, researchers first solve the Lq-norm (0 < q< 1) regularization problem to fast obtain the sparse images. Furthermore, for the purpose of suppressing azimuth ambiguity caused by limited pulse repetition frequency (PRF) and data down-sampling, an L2,1/2-norm regularization-based sparse SAR imaging method is proposed and applied to the unambiguous recovery of a large-scale sparse scene.
Flow diagram of the proposed method. Credit: Science China Press
Recovered images of large-scale scenes by (a) CSA from fully sampled data, (b) CSA from 50% down-sampled data,（c）L1-Sp from 50% down-sampled data，(d) L1/2-Sp from 50% down-sampled data, (e) L2,1-Sp from 50% down-sampled data, and (f) L2,1/2-Sp from 50% down-sampled data. Credit: Science China Press