Zhang Shuanghui, Liu Yongxiang, Li Xiang
School of Electronic Science and Engineering, National University of Defense Technology, Changsha 410073, China.
Sensors (Basel). 2017 Oct 10;17(10):2295. doi: 10.3390/s17102295.
Interferometric inverse synthetic aperture radar (InISAR) imaging for sparse-aperture (SA) data is still a challenge, because the similarity and matched degree between ISAR images from different channels are destroyed by the SA data. To deal with this problem, this paper proposes a novel SA-InISAR imaging method, which jointly reconstructs 2-dimensional (2-D) ISAR images from different channels through multiple response sparse Bayesian learning (M-SBL), a modification of sparse Bayesian learning (SBL), to achieve sparse recovery for multiple measurement vectors (MMV). We note that M-SBL suffers a heavy computational burden because it involves large matrix inversion. A computationally efficient M-SBL is proposed, which, proceeding in a sequential manner to avoid the time-consuming large matrix inversion, is denoted as sequential multiple sparse Bayesian learning (SM-SBL). Thereafter, SM-SBL is introduced to InISAR imaging to simultaneously reconstruct the ISAR images from different channels. Numerous experimental results validate that the proposed SM-SBL-based InISAR imaging algorithm performs superiorly against the traditional single-channel sparse-signal recovery (SSR)-based InISAR imaging methods in terms of noise suppression, outlier reduction and 3-dimensional (3-D) geometry estimation.
用于稀疏孔径（SA）数据的干涉逆合成孔径雷达（InISAR）成像仍然是一个挑战，因为SA数据会破坏来自不同通道的ISAR图像之间的相似性和匹配度。为了解决这个问题，本文提出了一种新颖的SA-InISAR成像方法，该方法通过对稀疏贝叶斯学习（SBL）的改进——多响应稀疏贝叶斯学习（M-SBL），从不同通道联合重建二维（2-D）ISAR图像，以实现对多个测量向量（MMV）的稀疏恢复。我们注意到M-SBL存在计算负担重的问题，因为它涉及大型矩阵求逆。本文提出了一种计算效率高的M-SBL，它以顺序方式进行以避免耗时的大型矩阵求逆，被称为顺序多稀疏贝叶斯学习（SM-SBL）。此后，将SM-SBL引入InISAR成像中，以同时重建来自不同通道的ISAR图像。大量实验结果验证了所提出的基于SM-SBL的InISAR成像算法在噪声抑制、异常值减少和三维（3-D）几何估计方面优于传统的基于单通道稀疏信号恢复（SSR）的InISAR成像方法。
