Efficient Time-Frequency Image Recovery Method For Incomplete Data of Micro-Motion Targets

Traditional time-frequency (TF) analysis methods for micro-motion targets face significant challenges, including low resolution, susceptibility to noise interference, and issues arising from incomplete data. Although sparse signal processing offers a useful solution to the problem of incomplete data, it is often accompanied by substantial computational and incomplete TF curves. To address these challenges, this article proposes an efficient TF image recovery method. Firstly, all-frame integrated recovery (AFIR) signal model is proposed, based on which the TF recovery optimization problem is resolved using the complex-valued alternating direction method of multipliers. The TF image of all frames is recovered simultaneously, which can avoid vectorization and reduce both computational and storage costs. Further, the AFIR network (AFIR-Net) architecture is proposed. By unfolding the optimization iteration process, the hyperparameters becomes learnable, and a lightweight complex U-Net prior learning module is integrated, enabling the hyperparameters and image prior to be co-optimized during training, enhancing the quality of the TF images and keeping high computational efficiency. Experimental results on both simulated and measured data indicate that the proposed method can successfully and efficiently generate high-quality TF images in scenarios of incomplete data and noise.