Target Fast Reconstruction of Real Aperture Radar Using Data Extrapolation-Based Parallel Iterative Adaptive Approach

Real aperture radar (RAR) usually sweeps a wide sector to continuously observe the scenario of interest. Because its angular resolution is limited by the size of the antenna aperture, target reconstruction methods are widely applied to obtain super-resolution radar images. However, the wide-sector processing mode suffers from high operational complexity because of the high-dimensional matrix inversion. Even worse, for the targets located at the scene edge, its echo data are received less than half beamwidth. The incomplete echo data will lead to the deformation of the reconstructed targets by existing reconstruction methods. To overcome the two problems, a data extrapolation-based parallel iterative adaptive approach is proposed to fast reconstruct the targets in the whole sector without the distortion at the scene edge. First, the echo model of RAR is repaired to remedy the model error. Then, based on the correlation of the echo data within one beamwidth, an autoregressive model is adopted to extrapolate the data of the missing half beamwidth. Finally, a parallel iterative adaptive approach method is proposed to efficiently recover the targets by exploiting the regular characteristics of the repaired steering matrix. Simulations and experimental data are applied to verify the proposed method.