Synthetic aperture radar (SAR) has become a state-of-the-art technology in many applications without being affected by changes in weather and daylight. Since the detection capability of the single-dimensional SAR is limited, the multidimensional (MD) SAR system, e.g., multibaseline and multipolarization, is used to improve its performance. The design of the MDSAR system should be directly related to the specified applications and a quantitatively analytical theory for bound analysis is required to achieve good efficiency. In this article, a mathematical framework for inversion bounds analysis of MDSAR is proposed. First, based on the attributed scattering center (ASC) model, the Fisher information matrix and its corresponding Cramer–Rao lower bound (CRLB) are used to get the error bound of the estimated parameters. Second, considering the discrete sampling (DS) of the parameters, a probability density function-based conversion is conducted to get the DS CRLB. Finally, the mathematical framework for MD acquisitions is established. The simulation-based experimental results show that the theoretical error bound is consistent with the output of the orthogonal matching pursuit (OMP). The error bound of the parameters obtained by the proposed general mathematical framework can be used to evaluate the performance of inversion algorithms under certain MDSAR configurations.

中文翻译：

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多维SAR散射体参数反演误差界分析


合成孔径雷达 (SAR) 已成为许多应用中最先进的技术，且不受天气和日光变化的影响。由于单维SAR的检测能力有限，因此采用多基线和多极化等多维（MD）SAR系统来提高其性能。 MDSAR系统的设计应与特定的应用直接相关，并且需要用于边界分析的定量分析理论才能实现良好的效率。本文提出了 MDSAR 反演边界分析的数学框架。首先，基于属性散射中心（ASC）模型，利用Fisher信息矩阵及其对应的Cramer-Rao下界（CRLB）得到估计参数的误差界。其次，考虑参数的离散采样（DS），进行基于概率密度函数的转换以获得DS CRLB。最后，建立了MD采集的数学框架。仿真实验结果表明，理论误差范围与正交匹配追踪（OMP）的输出一致。由所提出的通用数学框架获得的参数的误差界可用于评估特定MDSAR配置下反演算法的性能。