For non-cooperative communication, the symbol-rate estimation of digital communication signal is an important problem to be solved. In this letter, A new algorithm for the symbol-rate estimation of single-tone digital...For non-cooperative communication, the symbol-rate estimation of digital communication signal is an important problem to be solved. In this letter, A new algorithm for the symbol-rate estimation of single-tone digitally modulated signal (i.e. MPSK/QAM) is proposed. Firstly a section from the received signal is cut as the template, and then the signal is matched sectionwise by making use of the signal selfsimilarity. So a signal con- taining the information of symbol jumping is got, and the symbol-rate can be estimated by DFT (Discrete Fou- rier Transformation). The validity of the new method has been verified by experiments.展开更多
文摘For non-cooperative communication, the symbol-rate estimation of digital communication signal is an important problem to be solved. In this letter, A new algorithm for the symbol-rate estimation of single-tone digitally modulated signal (i.e. MPSK/QAM) is proposed. Firstly a section from the received signal is cut as the template, and then the signal is matched sectionwise by making use of the signal selfsimilarity. So a signal con- taining the information of symbol jumping is got, and the symbol-rate can be estimated by DFT (Discrete Fou- rier Transformation). The validity of the new method has been verified by experiments.
文摘为提高车辆控制算法对不同道路的适应能力,在原有学习预测控制算法的基础上,本文提出一种基于经验迁移的赛车学习预测控制策略.基于所建立的赛车曲线坐标系模型,记录小车在历史赛道上的行驶轨迹,将其作为采样安全集.采样安全集蕴含了车辆行驶的经验信息.在新赛道上,通过与采样安全集内曲率相近的轨迹进行特征匹配,找出新赛道的虚拟路径跟踪轨迹.然后,对虚拟路径跟踪轨迹附近的采样点进行坐标变换,将历史轨迹转换为新赛道的虚拟采样轨迹,实现对历史赛道上的行驶经验的迁移.构造了迁移学习预测控制(TLMPC),使小车在新的赛道上能够通过学习预测控制器以更快的速度行驶.本文在4个典型赛道上进行了仿真,结果表明所设计的控制策略控制效果有明显提升.与LMPC相比,10次迭代结果中单圈耗时至少减少了1.2 s.