In this paper, we employ the thoughts and methodologies of Shannon's information theory to solve the problem of the optimal radar parameter estimation. Based on a general radar system model, the \textit{a posteriori} probability density function of targets' parameters is derived. Range information (RI) and entropy error (EE) are defined to evaluate the performance. It is proved that acquiring 1 bit of the range information is equivalent to reducing estimation deviation by half. The closed-form approximation for the EE is deduced in all signal-to-noise ratio (SNR) regions, which demonstrates that the EE degenerates to the mean square error (MSE) when the SNR is tending to infinity. Parameter estimation theorem is then proved, which claims that the theoretical RI is achievable. The converse claims that there exists no unbiased estimator whose empirical RI is larger than the theoretical RI. Simulation result demonstrates that the theoretical EE is tighter than the commonly used Cram\'er-Rao bound and the ZivZakai bound.

翻译：本文运用香农信息论的思想与方法，解决最优雷达参数估计问题。基于通用雷达系统模型，推导出目标参数的后验概率密度函数。定义距离信息（RI）和熵误差（EE）作为性能评估指标。证明获取1比特距离信息等价于将估计偏差减半。推导出全信噪比（SNR）区间内熵误差的闭式近似，表明当信噪比趋于无穷时，熵误差退化为均方误差（MSE）。进而证明参数估计定理，指出理论距离信息是可实现的，其逆定理则表明不存在经验距离信息大于理论距离信息的无偏估计器。仿真结果表明，理论熵误差较常用Cram\'er-Rao界和Ziv-Zakai界具有更紧的边界。