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    This study introduces an efficient algorithm for sparse aperture Inverse Synthetic Aperture Radar (ISAR) autofocusing. By optimizing Sparse Bayesian Learning (SBL) with the alternating direction method of multipliers (ADMM), the method significantly accelerates real-time ISAR imaging.

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    Area of Science:

    • Radar Systems Engineering
    • Signal Processing
    • Computational Imaging

    Background:

    • Sparse aperture Inverse Synthetic Aperture Radar (ISAR) imaging typically relies on compressive sensing (CS) due to non-uniform sampling, which prevents standard Fast Fourier Transform (FFT) methods.
    • Existing CS-based ISAR autofocusing techniques are computationally intensive, hindering their application in real-time systems.

    Purpose of the Study:

    • To develop a computationally efficient algorithm for sparse aperture ISAR autofocusing.
    • To improve the practical applicability of sparse aperture ISAR imaging in real-time scenarios.

    Main Methods:

    • Image reconstruction using Sparse Bayesian Learning (SBL) to address sparse aperture effects.
    • Phase error estimation via minimum entropy during ISAR image reconstruction.
    • Optimization of SBL's matrix inversion (O(L3) complexity) using the alternating direction method of multipliers (ADMM) and an auxiliary variable, transforming it into element-wise matrix division.

    Main Results:

    • The proposed algorithm reconstructs ISAR images effectively while mitigating sparse aperture challenges.
    • The ADMM-based optimization significantly reduces computational complexity compared to standard SBL.
    • Experimental results demonstrate a 20-30 times speed improvement over traditional SBL methods for sparse aperture ISAR autofocusing.

    Conclusions:

    • The developed algorithm offers a highly efficient solution for sparse aperture ISAR autofocusing.
    • The integration of ADMM with SBL enhances computational performance, making real-time applications feasible.
    • The method is validated by both simulated and measured data, confirming its effectiveness and efficiency.