Sparse Reconstruction for Radar Imaging Based on Quantum Algorithms - Liu, Xiaowen; Dong, Chen; Luo, Ying; Kang, Le; Liu, Yong; Zhang, Qun (IEEE geoscience and remote sensing letters, 2022)
The sparse-driven radar imaging can obtain high-resolution images about a target scene with the down-sampled data. However, the huge computational complexity of the classical sparse recovery method for the particular situation seriously affects the practicality of the sparse imaging technology. In this letter, this is the first time the quantum algorithms are applied to the image recovery for the radar sparse imaging. First, the radar sparse imaging problem is analyzed and the calculation problem to be solved by quantum algorithms is determined. Then, the corresponding quantum circuit and its parameters are designed to ensure extremely low computational complexity, and the quantum-enhanced reconstruction algorithm for sparse imaging is proposed. Finally, the computational complexity of the proposed method is analyzed, and the simulation experiments with the raw radar data are illustrated to verify the validity of the proposed method.