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Efficient ISAR Phase Autofocus Based on Eigenvalue Decomposition
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DOI：10.1109/LGRS.2017.2746758.png)
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Abstract
En 中文
Phase autofocus is a key step in translational motion compensation for inverse synthetic aperture radar. From the eigenvalue decomposition (EVD) of the covariance matrix generated by the aligned range-compressed signal, eigenvectors can be obtained for effective phase autofocus. However, as the number of pulse samples is increased to improve the cross-range resolution, the high computational complexity of the EVD may become burdensome. To address this problem, we propose a novel EVD-based method in this letter. When the number of range units is larger than the number of pulse samples, the conventional method is used. Otherwise, the transpose of the envelope-aligned data matrix is used to generate a lower dimensional covariance matrix and to perform successive autofocus processing. Since many real targets exist in limited range units, a one-or two-order-higher computational efficiency can be obtained in some typical scenarios with the proposed method, compared with existing EVD-based approaches. Furthermore, the equivalence between the above two methods has been proven in this letter. Finally, the results for real measured data are provided to demonstrate the effectiveness of the proposed method.
Keywords:
Computational efficiency
eigenvalue decomposition (EVD)
inverse synthetic aperture radar (ISAR)
phase autofocus
translational motion compensation (TMC)
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