In the field of radar parameter estimation, Cramer-Rao bound (CRB) is a commonly used theoretical limit. However, CRB is only achievable under high signal-to-noise (SNR) and does not adequately characterize performance in low and medium SNRs. In this paper, we employ the thoughts and methodologies of Shannon's information theory to study the theoretical limit of radar parameter estimation. Based on the posteriori probability density function of targets' parameters, joint range-scattering information and entropy error (EE) are defined to evaluate the performance. The closed-form approximation of EE is derived, which indicates that EE degenerates to the CRB in the high SNR region. For radar ranging, it is proved that the range information and the entropy error can be achieved by the sampling a posterior probability estimator, whose performance is entirely determined by the theoretical posteriori probability density function of the radar parameter estimation system. The range information and the entropy error are simulated with sampling a posterior probability estimator, where they are shown to outperform the CRB as they can be achieved under all SNR conditions
翻译:在雷达参数估计领域,Cramer-Rao绑定(CRB)是一个常用的理论限制。然而,CRB只有在高信号到噪音(SNR)下才能实现,并且没有适当描述中低信号到噪音(SNR)的性能。在本文中,我们使用香农信息理论的想法和方法来研究雷达参数估计的理论限度。根据目标参数的事后概率密度功能、联合射程感应信息以及诱导误差(EEE)定义来评估性能。EE的闭式近似是导出的结果,表明EEE在高信号到噪音(SNR)区域向CRB变形。关于雷达测距,可以证明通过取样一个远光概率估测仪,其性能完全由雷达参数估计系统的理论后位概率密度函数决定,其性能完全由测得。范围信息和诱导误值通过取样后概率估测算器模拟出,显示在SRB的所有条件下均能超越CRB。