MAFFormer: A Deep Learning-Based Multiaperture Fusion Algorithm for Near-Field Imaging

Near-field radar systems are widely used in military and industrial applications for the detection and high-precision imaging of complex objects. Achieving precise delineation of target features requires high angular resolution, a critical metric directly related to array aperture. Distributed radar networks obtain a large-aperture array and enhanced angular resolution by coherently combining signals from subapertures positioned at various locations. However, the spacing between subapertures often leads to sparse spatial sampling, introducing grating lobes into the reconstructed image. In this article, we propose a multiaperture fusion transformer (MAFFormer), a complex-valued (CV) dual-domain, local–global feature fusion neural network for multiaperture fusion in near-field distributed radar networks, which enables accurate recovery of missing apertures under 75% signal sparsity. By leveraging both local and global features from the antenna array and spatial spectrum domains (S-domains) of raw radar signal through the integration of convolutional neural network (CNN) and Transformer components, the proposed network achieves enhanced estimation accuracy, overcoming the limitations of current algorithms that suffer from performance degradation in complex environments. Extensive experiments, including simulations and measurements, validate that the proposed algorithm achieves high-resolution imaging of complex targets without additional physical apertures or complex hardware and shows significant superiority on imaging quality compared to traditional methods across diverse target types. Furthermore, the robustness of the proposed structure to unseen radar parameters and imaging scenarios is validated using publicly datasets, highlighting its suitability for a wide range of practical imaging applications.