Sparse Reconstruction for Radar Imaging based on Quantum Algorithms

TL;DR

This paper introduces a novel quantum algorithm for radar sparse imaging that significantly reduces computational complexity, enabling high-resolution target scene reconstruction from limited data.

Contribution

First application of quantum algorithms to radar sparse imaging, designing quantum circuits for efficient image recovery with low computational complexity.

Findings

Quantum-enhanced reconstruction algorithm demonstrated effective image recovery.

Simulation results verify the validity and efficiency of the proposed quantum approach.

Complexity analysis shows substantial reduction compared to classical methods.

Abstract

The sparse-driven radar imaging can obtain the high-resolution images about 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 paper, this is the first time the quantum algorithms are applied to the image recovery for the radar sparse imaging. Firstly, 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.