Signal-to-noise ratio (SNR) is an important metric for performance assessment in numerous scenerios. In order to ensure the reliability and effectiveness of the system performance, plenty of situations require the information of SNR estimate. At the same time, Mars exploration has been a hot topic in recent years, which leads to the research attention of scholars extending to deep space. In this paper, a new SNR estimator related to deep space scene is proposed. On the one hand, the time of essential data transmission is limited in Mars exploration system. On the other hand, the relative position and condition between orbiters vary quickly all the time, which makes it difficult to obtain the accurate and significant information for Mars exploration. Therefore, it is obvious that the information of SNR can promote the system to adjust the signal transmission rate automatically. Subsequently, the estimation of SNR becomes a fundamental research in automatic digital communications. In this paper, an SNR estimation method based on non-data-aided (NDA) with maximum likelihood (ML) estimation is proposed to enhance the accuracy and reliability of Mars exploration process. Additionally, the Cramer-Rao lower bound (CRLB) related to the designed ML algorithm is derived. Finally, the Monte Carlo simulation results demonstrate that the proposed ML estimator algorithm obtains a superior performance when compared to the existing SNR estimators.

信噪比（SNR）是在众多场景中进行性能评估的重要指标。为了确保系统性能的可靠性和有效性，很多情况下都需要SNR估计信息。同时，火星探测已成为近年来的热门话题，引起了学者们对深空研究的关注。本文提出了一种新的与深空场景有关的信噪比估计器。一方面，火星探测系统中必不可少的数据传输时间受到限制。另一方面，轨道器之间的相对位置和状态一直在迅速变化，这使得难以获得准确和重要的火星探测信息。因此，显然，信噪比信息可以促进系统自动调整信号传输速率。随后，SNR的估计成为自动数字通信的基础研究。为了提高火星探测过程的准确性和可靠性，提出了一种基于最大似然估计的非数据辅助（NDA）估计方法。另外，推导了与设计的ML算法有关的Cramer-Rao下界（CRLB）。最后，蒙特卡罗仿真结果表明，与现有的SNR估计器相比，所提出的ML估计器算法具有更高的性能。为了提高火星探测过程的准确性和可靠性，提出了一种基于非数据辅助最大似然估计的信噪比估计方法。另外，推导了与设计的ML算法有关的Cramer-Rao下界（CRLB）。最后，蒙特卡罗仿真结果表明，与现有的SNR估计器相比，所提出的ML估计器算法具有更高的性能。为了提高火星探测过程的准确性和可靠性，提出了一种基于非数据辅助最大似然估计的信噪比估计方法。另外，推导了与设计的ML算法有关的Cramer-Rao下界（CRLB）。最后，蒙特卡罗仿真结果表明，与现有的SNR估计器相比，所提出的ML估计器算法具有更高的性能。