空间目标T/R-R型雷达多视角融合成像算法

Multi-view fusion imaging algorithm for T/R-R radar of space targets

针对T/R-R(transmitting receiving-receiving)构型雷达成像实际,结合空间目标的轨道先验和双基地逆合成孔径雷达(inverse synthetic aperture radar,ISAR)成像的体制优势,提出了一种基于复贝叶斯压缩感知(Bayesian compressed sensing,BCS)算法的T/R-R型雷达稀疏孔径多视角融合成像方法。所提方法在建立双站雷达融合成像模型的基础上,利用Laplace先验在复数域建立目标的稀疏模型,提高了算法的稀疏促进作用,获得了高分辨目标图像。仿真实验结果表明,所提方法不仅可实现双站雷达多视角的融合成像,还可实现双站雷达各自存在稀疏孔径条件下的多视角融合成像,进一步拓展了应用场景,可有效提高方位分辨率和成像质量。

A T/R-R(transmitting receiving-receiving)radar sparse aperture multi-view fusion imaging method based on the complex Bayesian compressed sensing(BCS)algorithm is proposed for the practical imaging of T/R-R configuration radar,taking into account the orbital priors of space targets and the advantages of the dual-station inverse synthetic aperture radar(ISAR)imaging system.On the basis of establishing a dual station radar fusion imaging model,the proposed method utilizes Laplace priors to establish a sparse model of the target in the complex domain,improving the sparsity promotion effect of the algorithm and obtaining high-resolution target images.The simulation experiment results show that the proposed method can not only achieve multi-view fusion imaging of dual station radar,but also achieve multi-view fusion imaging of dual-station radar with sparse aperture respectively,further expanding the application scenarios and effectively improving azimuth resolution and imaging quality.