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arXiv 2309.08038eess.SP
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通过稳健稀疏阵列合成实现高效的旋转合成孔径雷达成像

Efficient Rotating Synthetic Aperture Radar Imaging via Robust Sparse Array Synthesis

Wei Zhao, Cai Wen, Quan Yuan, Rong Zheng

AI总结:

本文提出一种基于稳健稀疏阵列合成的ROSAR高效成像方法,通过离线计算稀疏孔径与滤波权重并引入主旁瓣稳健约束,利用非凸优化求解,在保持图像质量的同时将计算时间减少达90%。

摘要:

旋转合成孔径雷达(ROSAR)可以利用从单一运动轨道收集的数据生成其周围环境的360度图像。由于其非线性的轨道,后向投影算法(BPA)通常被用于在ROSAR中生成SAR图像。尽管BPA具有出色的成像性能,但其计算复杂度较高,限制了其在实时系统中的应用。本文提出了一种基于稳健稀疏阵列合成的高效成像方法。该方法首先进行距离维匹配滤波,随后使用选定的稀疏孔径和滤波权重进行方位维匹配滤波。孔径和权重提前离线计算,以确保对由雷达旋转不完美引起的阵列流形误差具有稳健性。我们在滤波器设计的主瓣和旁瓣电平上引入了稳健约束。由此产生的稳健稀疏阵列合成问题是一个带有二次约束的非凸优化问题。设计了一种基于可行点追踪和逐次凸逼近的算法来求解该优化问题。广泛的仿真研究以及使用真实硬件平台的实验评估表明,所提出的算法能够实现与BPA相当的图像质量,但计算时间最多减少了90%。

英文摘要:

Rotating Synthetic Aperture Radar (ROSAR) can generate a 360$^\circ$ image of its surrounding environment using the collected data from a single moving track. Due to its non-linear track, the Back-Projection Algorithm (BPA) is commonly used to generate SAR images in ROSAR. Despite its superior imaging performance, BPA suffers from high computation complexity, restricting its application in real-time systems. In this paper, we propose an efficient imaging method based on robust sparse array synthesis. It first conducts range-dimension matched filtering, followed by azimuth-dimension matched filtering using a selected sparse aperture and filtering weights. The aperture and weights are computed offline in advance to ensure robustness to array manifold errors induced by the imperfect radar rotation. We introduce robust constraints on the main-lobe and sidelobe levels of filter design. The resultant robust sparse array synthesis problem is a non-convex optimization problem with quadratic constraints. An algorithm based on feasible point pursuit and successive convex approximation is devised to solve the optimization problem. Extensive simulation study and experimental evaluations using a real-world hardware platform demonstrate that the proposed algorithm can achieve image quality comparable to that of BPA, but with a substantial reduction in computational time up to 90%.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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