基于卷积ADMM网络的高效结构化稀疏ISAR成像方法

结构化稀疏逆合成孔径雷达(inverse synthetic aperture radar, ISAR)成像是空间态势感知与目标识别的重要手段。该问题可通过压缩感知(compressive sensing, CS)方法解决。目前, 许多传统CS方法仍存在运算效率低、参数适应性不强等问题。针对该问题, 本文提出了一种基于卷积交替方向乘子法网络(convolutional alternating direction method of multipliers network, C-ADMMN)的结构化稀疏ISAR成像方法。利用深度展开方法, 结合传统结构化稀疏ISAR成像模型, 构建C-ADMMN网络。通过监督学习, C-ADMMN仅需约10层网络便可达到传统方法上百次迭代的效果, 具有较高的运算效率且对不同目标具有一定适应性。基于仿真与实测数据的实验结果验证了网络的高效性与参数适应性。

Computational efficient structural sparse ISAR imaging method based on convolutional ADMM-net

Structural sparse inverse synthetic aperture radar (ISAR) imaging is an important approach for situation awareness and object recognition. It can be solved via compressive sensing (CS) methods. At present, many conventional CS algorithms still suffer from low computational efficiency and poor parameter adaptability. In this paper, a structural sparse ISAR imaging method based on convolutional alternating direction method of multipliers network (C-ADMMN) is proposed to overcome those problems. The network is established via deep unfolding methods combined with traditional structural sparse ISAR imaging models. The network only needs approximate 10 layers to achieve the effect of hundreds of iterations in traditional methods through supervised learning. The network achieves higher computing efficiency and has a certain sdaptability to different goals, which is proved on the experiment results based on simulated and measured data.