Accurate vehicle speed estimation is required for intelligent internet of vehicles, and it can be realized by Doppler shift estimation in mobile communication. In this paper, the ACF-DSR (autocorrelation function-double sampling rate) method for joint Doppler shift and SNR estimation is further investigated. Based on the analysis of ACF, an improved algorithm model taking account of estimation deviation is formulated. Then, the effects of sampling intervals in DSR are figured out by mean square error analysis of estimation, and two better choices are obtained. By Monte Carlo simulations, it is demonstrated that the optimized ACF-DSR method with better choice of sampling intervals can achieve better estimation and outperform previous methods.

智能车联网需要准确的车速估计，可以通过移动通信中的多普勒频移估计来实现。在本文中，进一步研究了用于联合多普勒频移和SNR估计的ACF-DSR（自相关函数-双采样率）方法。在对ACF分析的基础上，提出了一种考虑估计偏差的改进算法模型。然后，通过估计的均方误差分析，找出了DSR中采样间隔的影响，得到了两个更好的选择。通过蒙特卡罗模拟，证明了优化的 ACF-DSR 方法具有更好的采样间隔选择可以实现更好的估计并优于以前的方法。