Efficient FMCW Radar Interference Suppression via Adaptive Learning Framework With Localized Sparse Prior

Frequency-modulated continuous-wave (FMCW) radar systems suffer from severe mutual interference in dense low-altitude uncrewed aerial vehicle (UAV) environments, critically degrading target detection fidelity and spectral efficiency in shared microwave bands. To address the challenges above, this article proposes an adaptive perceptual learning target/interference network, named APLF-Net. Initially, a multivariate coupled perception model is proposed to co-optimize the objective function with auxiliary sparse priors. Subsequently, a stepwise alternating optimization framework is designed and integrated into a hierarchical deep feedforward network for enhancing flexibility. Within this framework, the hierarchical network structure is refined to mimic optimization steps, accelerating the model data processing. The threshold convolution module based on echo energy inference is proposed to prevent excessive sparsification. Furthermore, a novel unsupervised training strategy is introduced, which leverages the intrinsic connections within the signal model to invert the sparse representations output by the network back into the original input echo, thereby completing the loss term mapping. A series of simulations and real-world experiments demonstrates that, compared to existing state-of-the-art methods, APLF-Net can rapidly suppress interference and accurately extract targets, exhibiting good performance.