A Novel 3-D Radar Imaging Method Based on Sparse Optimization

Three-dimensional (3-D) radar imaging is an important task underpinning many applications that seek target detection, localization, and classification. However, traditional 3-D radar imaging is computationally cumbersome and needs a large memory space to store the cubic data matrix. These challenges have hindered 3-D radar imaging development and limited its applications. Owing to the sparsity of the radar image of typical targets, the data size could be significantly reduced by exploiting compressive sensing and sparse reconstruction techniques. These techniques prove important to mitigate the sidelobe levels that arise from successive Fourier-based processing. Towards this end, we first perform polar reformatting to the 2-D data matrix in azimuth and range. Then, a series of images over different values of the vertical variable z are generated by using different focusing filters. Sparse optimization is afterwards applied to the 3-D data cube to produce high-resolution, significantly reduced sidelobe 3-D image. Compared with the conventional 3-D radar imaging methods, the proposed method, in addition to high fidelity images, requires fewer data measurements and offers computationally efficient processing. Numerical simulations are provided to evaluate the effectiveness of the proposed method.