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Pattern Coupled Sparse Bayesian Learning Based on UTAMP for Robust High Resolution ISAR Imaging
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DOI：10.1109/JSEN.2020.3004037.png)
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Block sparse Bayesian learning (BSBL) has been widely used in inverse synthetic aperture radar (ISAR) imaging, which significantly improves the imaging performance by exploiting the sparse pattern information of ISAR images. However, the conventional Bayesian learning algorithm has high computational complexity, which hinders its applications to real-time processing of radar imaging. The approximate message passing (AMP) can be used to obtain a low complexity implementation of sparse Bayesian learning (SBL). However, AMP suffers from performance losses and even diverges in the case of high-Doppler resolution ISAR imaging where the measurement matrix can be highly correlated. To solve this problem, we propose a fast pattern coupled SBL ISAR imaging algorithm based on approximate message passing with unitary transformation (UTAMP). First, the estimates of the hyperparameters of sparse vector are obtained through UTAMP based SBL, and then nearest neighbor hyperparameters are coupled and updated for next iteration. With low complexity, the proposed algorithm can effectively exploit the sparse pattern information of ISAR images, and exhibits excellent convergence and imaging performance. Both simulation and real data experiments are carried out to verify the effectiveness of the proposed algorithm.
Keyword:
Imaging
Radar imaging
Approximation algorithms
Computational complexity
Bayes methods
Sensors
Image resolution
ISAR imaging
sparse Bayesian learning
block sparse
UTAMP
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4.5
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2.1W
被引数:
7.3W
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Pattern-Coupled Sparse Bayesian Learning for Inverse Synthetic Aperture Radar Imaging模式耦合稀疏贝叶斯学习在逆合成孔径雷达成像中的应用
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