ISAR Sparse Imaging Based on Deep Augmented IST Network
Addressing the issues of parameter sensitivity and slow convergence in traditional Inverse Synthetic Aperture Radar(ISAR)sparse imaging algorithms,inspired by the adaptive parameter learning mechanism of convolutional neural networks and combining the physical interpretability of model-driven networks,a new ISAR sparse imaging framework known as the Deep Augmented-Iterative Shrinkage Thresholding(DA-IST)network is proposed.Firstly,the DA-IST net-work maps the iterative steps of the Iterative Shrinkage Thresholding Algorithm(ISTA)into the hidden layers,which not only improves interpretability but also enables learning optimal parameters during training.Secondly,the network takes into account neglected high-frequency components during modeling,enhancing reconstruction performance.Additionally,to improve the network's robustness,nonlinear convolutional layers are employed to replace linear sparse transformations.Experimental results demonstrate that,compared to traditional model-driven algorithms,the DA-IST network eliminates the need for manual parameter tuning,exhibits faster convergence,produces higher-quality imaging,and possesses better generalization capabilities for data with significant feature differences.
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