A Sparse Super-Resolution Imaging Approach for Array Scanning Radar in High-Resolution Ground Mapping

In airborne sensing applications, radar forward-looking imaging is a crucial technology for high-resolution ground mapping and terrain perception. Super-resolution deconvolution is key to overcoming the real-beam resolution limits of these airborne sensors. However, when utilizing phased array scanning radars for wide-swath ground mapping, the antenna pattern exhibits severe spatial variation at large scanning angles, which directly leads to model mismatch and degradation in super-resolution performance. To address this hardware-induced sensing limitation, this paper proposes a sparse super-resolution method tailored for forward-looking phased array scanning radar. Firstly, the causes of the spatial variation in antenna pattern are analyzed, and a modified antenna convolution matrix is derived to accurately model the scanning process. Secondly, the corresponding objective function is formulated under the assumption of target sparsity. Finally, an alternating direction method of multipliers (ADMM) solver based on reweighted strategy is employed to resolve the objective function. Experimental results demonstrate that the proposed method achieves approximately a 4 times increase in cross-range resolution and effectively enhances the observation capabilities within the radar forward-looking area.