School of Engineering, University of South Australia, Mawson Lakes, SA 5095, Australia.
National Security and ISR Division, Defence Science and Technology Group, Edinburgh, SA 5111, Australia.
Sensors (Basel). 2018 Jun 5;18(6):1840. doi: 10.3390/s18061840.
This paper presents a new image focusing algorithm for sparsity-driven radar imaging of rotating targets. In the general formulation of off-grid scatterers, the sparse reconstruction algorithms may result in blurred and low-contrast images due to dictionary mismatch. Motivated by the natural clustering of atoms in the sparsity-based reconstructed images, the proposed algorithm first partitions the atoms into separate clusters, and then the true off-grid scatterers associated with each cluster are estimated. Being a post-processing technique, the proposed algorithm is computationally simple, while at the same time being capable of producing a sharp and correct-contrast image, and attaining a scatterer parameter estimation performance close to the Cramér⁻Rao lower bound. Numerical simulations are presented to corroborate the effectiveness of the proposed algorithm.
本文提出了一种新的稀疏驱动雷达旋转目标成像聚焦算法。在非网格散射体的一般公式中，由于字典不匹配，稀疏重建算法可能导致图像模糊和对比度低。受基于稀疏重建图像中原子自然聚类的启发，所提出的算法首先将原子分成单独的簇，然后估计与每个簇相关的真实非网格散射体。作为一种后处理技术，所提出的算法计算简单，同时能够产生清晰和正确对比度的图像，并实现接近克拉美-罗下限的散射体参数估计性能。数值模拟验证了所提出算法的有效性。
