基于改进SARA的综合孔径射电望远镜成像算法

Imaging algorithm of synthetic aperture radio telescope based on improved SARA

综合孔径射电望远镜在射电天文领域具有重要的应用价值.然而,综合孔径射电望远镜反演成像过程是病态的反问题.虽然以稀疏平均重加权分析(Sparsity Averaging Reweighted Analysis,SARA)算法为代表的压缩感知技术已经成功应用于综合孔径射电望远镜成像中,但是传统的SARA算法存在着软阈值参数难以选择的问题,依然存在较大的重构误差.为此,提出一种改进的SARA算法.该算法利用一种改进的投影快速软阈值迭代算法求解最小化模型,在迭代过程中充分利用数据保真项和正则化项的信息自适应地更新软阈值参数,并采取重启和自适应策略来加速收敛,以提高反演成像的精度和速度.仿真结果表明,与传统的压缩感知算法相比,改进SARA算法能有效地降低重构误差并提高计算速度,证明了其有效性.

Synthetic aperture radio telescopes are important in the field of radio astronomy.However,the inverse imaging process of these telescopes is an ill-posed inverse problem.Although the compressed sensing technology represented by the sparsity averaging reweighted analysis(SARA)algorithm has been successfully applied to the imaging of the synthetic aperture radio telescope,large reconstruction errors remain in the traditional SARA algorithm due to its difficulty in selecting soft threshold parameters.Therefore,an improved SARA algorithm is proposed.This algorithm uses an improved projected fast iterative soft thresholding algorithm to solve the minimization model,adaptively updates soft threshold parameters by fully utilizing data fidelity and regularization terms,and adopts restarting and adaptive strategies to accelerate convergence,thereby improving the overall accuracy and speed of inversion imaging.The simulation results demonstrate that compared with the traditional compressed sensing algorithm,the improved SARA algorithm can effectively reduce reconstruction error and increase calculation speed,thus proving its effectiveness.