For radar network, each radar can obtain the two-dimensional (2D) inverse synthetic aperture radar (ISAR) image from the corresponding observation angle independently. Taking advantage of the multi-view observation via radar network and the projection relationship in ISAR imaging, the three-dimensional (3D) image of the target can be reconstructed by the inverse-projection principle. However, it is hard to reconstruct the scattering position and coefficient simultaneously, and the network optimization should be studied to improve the reconstruction performance. To solve these problems, a novel 3D scattering image reconstruction method is proposed in this paper. Firstly, the 3D reconstruction model is built as a compressed sensing (CS) framework. Then, the network optimization for single target is transformed into the measurement matrix optimization before the 3D scattering recovery. Finally, numerical simulations under the noise scenarios and the principle prototype experiment on real data are shown to demonstrate the validity of the proposed method.

对于雷达网络，每个雷达都可以从相应的观察角度独立获得二维（2D）逆合成孔径雷达（ISAR）图像。利用ISAR成像中通过雷达网络进行的多视图观测和投影关系，可以利用反投影原理重建目标的三维（3D）图像。但是，很难同时重建散射位置和系数，应该研究网络优化以提高重建性能。为了解决这些问题，本文提出了一种新颖的3D散射图像重建方法。首先，将3D重建模型构建为压缩感知（CS）框架。然后，在3D散射恢复之前，将单个目标的网络优化转换为测量矩阵优化。最后，在噪声场景下进行了数值模拟，并对真实数据进行了原理原型实验，证明了该方法的有效性。