An Efficient Method on ISAR Image Reconstruction via Norm Regularization

Inverse synthetic aperture radar (ISAR) imaging technology is a powerful tool to distinguish between targets with different types. The high-resolution radar image acquisition is an important basis for further automatic target recognition. In the high-frequency radar band, since the spatially collected received signals have strong sparsity in the image domain (Fourier domain), they can be downsampled and restored by corresponding norm regularization framework. When the target is electrically large, its contributions are equal to several point scatterers [also called scattering centers (SCs)]. So that the imaging process can be regarded as the summation of the point scatterers impulse response, the convolution process, and deconvolution method, the reverse process is adopted to extract the SCs from the image, where the number of SCs reflects the sparseness of ISAR image. As for the reconstruction method, while L
₁
regularization is widely used, the L
_q
(0 <; q <; 1) regularization has proved to be a more sparse regularization framework and achieves better performance in reconstructing a signal with a lower down-sampling rate. This article implements L
₁
regularization into the ISAR image reconstruction 2 under down-sampling. The ISAR image is generated by patch integration formula from the physical optics method. The sparsity of the ISAR image is estimated through the appropriate deconvolution process CLEAN. Given the sparsity estimated, and compared with the L
₁
regularization, the experiment of two complex geometry targets validates the reconstruction precision and convergence and lossless recovery probability of L
_1/2
regularization.