A high-resolution learning imaging method for THz-SAR moving targets based on AF-RPCA-Net

• In response to the azimuth defocusing issues caused by target motion, a sparse recovery model capable of characterizing target phase errors is established, and an autofocusing module based on the maximum contrast criterion is proposed, achieving the estimation of azimuth phase errors. • Considering the low-rank characteristics of the THz-SAR moving target range profile matrix, the dispersed nature of the background range profile, and the sparsity of the moving target image, the concept of Robust Principal Component Analysis (RPCA) is introduced. By integrating the aforementioned autofocusing module, a THz-SAR moving target imaging algorithm based on low-rank and sparsity joint constraints, AF-RPCA-ADMM, is proposed, achieving the separation of moving targets from the background and focused imaging. • To accelerate the imaging algorithm, an efficient HR imaging network for moving targets, AF-RPCA-Net, based on a deep unfolding network, is designed. The network automatically learns the optimal hyperparameters required for each iteration process of the sparse recovery algorithm, resulting in well-focused THz-SAR moving target images efficiently. Terahertz-Synthetic Aperture Radar (THz-SAR) offers high frame rates and high resolution, making it particularly suitable for remote sensing applications, like dynamic monitoring of moving targets. However, due to the non-ideal motion of the airborne platform and the non-cooperative motion of targets, this phenomenon causes more severe defocusing compared with microwave band SAR. Traditional SAR imaging methods, if directly applied to image THz-SAR moving targets, often suffer from poor quality and low efficiency. To address this issue, this article proposes a moving target non-parametric learning imaging method based on the Deep Unfolding Network (DUN) framework. Firstly, an autofocusing module is derived based on the maximum imaging contrast and embedded within the Alternating Direction Method of Multipliers (ADMM) iterative solution process to achieve accurate compensation of azimuthal motion errors. Then, we introduce the concept of Robust Principal Component Analysis (RPCA) to achieve sparse recovery imaging of moving targets. Finally, based on the ADMM iterative solution process, we establish an imaging network, named AF-RPCA-Net, efficiently achieving model-data jointly driven moving target background separation and imaging. The proposed method is validated to be effective and efficient through experimental results derived from both simulated and measured data.