L-1-Regularization-Based SAR Imaging and CFAR Detection via Complex Approximated Message Passing
L-1-Regularization-Based SAR Imaging and CFAR Detection via Complex Approximated Message Passing
复制标题
通过复杂近似消息传递进行基于 L-1 正则化的 SAR 成像和 CFAR 检测
DOI：
10.1109/tgrs.2017.2671519
复制
发表时间：
2017
影响因子：
8.2
通讯作者：
Wu Yirong
中科院分区：
文献类型：
--
作者：
Bi Hui;Zhang Bingchen;Zhu Xiao Xiang;Hong Wen;Sun Jinping;Wu Yirong
合成孔径雷达（SAR）是一种广泛应用的高分辨率主动微波成像技术，具有全天时、全天候的侦察能力。与传统的基于匹配滤波（matched filtering，MF）的方法相比，Lq（0 ≤ q ≤ 1）正则化方法可以有效地改善SAR成像性能，抑制旁瓣和杂波。然而，传统的基于Lq正则化的SAR成像方法需要将二维回波数据转换为矢量，并通过二维矩阵运算重建场景。这导致与MF相比显著更多的计算复杂度，并且使得其非常难以应用于高分辨率和宽测绘带成像。典型的Lq正则化恢复算法，例如，迭代阈值算法，可以改善成像性能的明亮的目标，但不能很好地保持图像的背景分布。因此，图像背景的几何属性为基础的应用，如恒虚警率（CFAR）检测，不能应用于正则化恢复SAR图像。另一方面，复近似消息传递（CAMP）是一种L1正则化重构的迭代恢复算法，它不仅可以像典型的正则化恢复算法那样实现原始信号的稀疏估计，而且可以同时实现非稀疏解。本文提出了两种新的基于CAMP的SAR成像算法，分别针对原始数据和复杂的雷达图像数据，沿着CFAR检测通过CAMP恢复的非稀疏结果。所提出的方法不仅可以提高SAR图像的性能，传统的L1正则化技术，但也有效地减少了计算量。而只有当我们有MF恢复SAR复图像，而不是原始数据，所提出的方法复图像数据可以实现类似的重建图像质量的正则化为基础的SAR成像方法使用完整的原始数据。本文的最重要的贡献是，提出的基于CAMP的方法使基于正则化重建SAR图像的CFAR检测可能使用其非稀疏场景估计，这具有类似的背景统计分布的MF恢复图像。实验结果验证了所提方法的有效性和恢复后的非稀疏图像用于恒虚警检测的可行性。
Synthetic aperture radar (SAR) is a widely used active high-resolution microwave imaging technique that has alltime and all-weather reconnaissance ability. Compared with traditionally matched filtering (MF)-based methods, Lq(0 ≤ q ≤ 1) regularization technique can efficiently improve SAR imaging performance e.g., suppressing sidelobes and clutter. However, conventional Lq-regularization-based SAR imaging approach requires transferring the 2-D echo data into a vector and reconstructing the scene via 2-D matrix operations. This leads to significantly more computational complexity compared with MF, and makes it very difficult to apply in high-resolution and wide-swath imaging. Typical Lqregularization recovery algorithms, e.g., iterative thresholding algorithm, can improve imaging performance of bright targets, but not preserve the image background distribution well. Thus, image background statistical-property-based applications, such as constant false alarm rate (CFAR) detection, cannot be applied to regularization recovered SAR images. On the other hand, complex approximated message passing (CAMP), an iterative recovery algorithm for L1regularization reconstruction, can achieve not only the sparse estimation of the original signal as typical regularization recovery algorithms but also a nonsparse solution simultaneously. In this paper, two novel CAMP-based SAR imaging algorithms are proposed for raw data and complex radar image data, respectively, along with CFAR detection via the CAMP recovered nonsparse result. The proposed method for raw data can not only improve SAR image performance as conventional L1regularization technique but also reduce the computational cost efficiently. While only when we have MF recovered SAR complex image rather than raw data, the proposed method for complex image data can achieve a similar reconstructed image quality as the regularization-based SAR imaging approach using the full raw data. The most important contribution of this paper is that the proposed CAMP-based methods make CFAR detection based on the regularization reconstruction SAR image possible using their nonsparse scene estimations, which has a similar background statistical distribution as the MF recovered images. The experimental results validated the effectiveness of the proposed methods and the feasibility of the recovered nonsparse images being used for CFAR detection.