Hao Hongxing, Zhu Wenjie, Yu Ronghuan, Liu Desheng
National Key Laboratory of Space Awareness, Space Engineering University, No.1 Bayi Road, Huairou, Beijing 101400, China.
Sensors (Basel). 2025 May 7;25(9):2943. doi: 10.3390/s25092943.
Inverse synthetic aperture radar (ISAR) technology is widely used in the field of target recognition. This research addresses the image reconstruction error in sparse imaging for bistatic radar systems. In this paper, sparse imaging technology is studied, and a sparse imaging recovery algorithm based on an improved Alternating Direction Method of Multipliers is proposed. The algorithm accelerates the convergence of the algorithm by dynamically adjusting iterative parameters in the iterative process. Experiments show that the algorithm proposed in this paper has lower relative recovery error in the case of different noise levels and sparsity, and it can be concluded that the algorithm proposed in this paper has a lower relative recovery error than the ADMMs (Alternating Direction Method of Multipliers).
逆合成孔径雷达(ISAR)技术在目标识别领域得到了广泛应用。本研究针对双基地雷达系统稀疏成像中的图像重建误差问题展开。本文对稀疏成像技术进行了研究,并提出了一种基于改进乘子交替方向法的稀疏成像恢复算法。该算法通过在迭代过程中动态调整迭代参数来加速算法收敛。实验表明,本文提出的算法在不同噪声水平和稀疏度情况下具有较低的相对恢复误差,并且可以得出本文提出的算法比乘子交替方向法(ADMMs)具有更低的相对恢复误差。