Tomographic synthetic aperture radar (TomoSAR) obtains elevation resolution by adding multiple baselines successively in the direction perpendicular to the line of sight, thereby realizing three-dimensional (3D) reconstruction of complex scenes and significantly promoting the development of the 3D application field. However, a large data redundancy and long mapping time in traditional 3D imaging lead to a large data transmission burden, low efficiency, and high costs. To solve the above problems, this paper proposes a distributed SAR high-resolution and wide-swath (HRWS) 3D imaging technology scheme. The proposed scheme overcomes the size limitation of traditional single-satellite antennas through the multi-channel arrangement of multiple satellites in the elevation direction to achieve HRWS imaging; meanwhile, the distributed SAR system is integrated with tomographic processing technology to realize 3D imaging of difficult areas by using the elevation directional resolution of TomoSAR systems. HRWS 3D SAR increases the baseline length and channel number by transmission in turn, which leads to excessive pulse repetition frequency and causes echoes of different pulse signals to overlap in the same receiving cycle, resulting in range ambiguity and thus seriously affecting the quality of the 3D reconstruction. To solve this problem, this paper proposes a range ambiguity resolution algorithm based on multi-beam forming and verifies it on the measured data from airborne array SAR. Compared with the traditional TomoSAR, the distributed HRWS 3D SAR scheme proposed in this paper can obtain a greater mapping bandwidth with the same resolution in a single flight, thereby enhancing the time correlation, reducing data redundancy, and greatly improving mapping efficiency.

Layover detection is crucial in 3D array SAR topography reconstruction. However, existing algorithms are not automated and accurate enough in practice. To solve this problem, this paper proposes a novel layover detection method that combines the complex-valued (cv) neural network and expert knowledge to extract features in the amplitude and phase of multi-channel SAR. First, inspired by expert knowledge, a fast Fourier transform (FFT) residual convolutional neural network was developed to eliminate the training divergence of the cv network, deepen networks without extra parameters, and facilitate network learning. Then, another innovative component, phase convolution, was designed to extract phase features of the layover. Subsequently, various cv neural network components were integrated with FFT residual learning blocks and phase convolution on the skeleton of U-Net. Due to the difficulty of obtaining SAR images marked with layover truths, a simulation was performed to gather the required dataset for training. The experimental results indicated that our approach can efficiently determine the layover area with higher precision and fewer noises. The proposed method achieves an accuracy of 97% on the testing dataset, which surpasses previous methods.