ID	原文	译文
54297	在未知干扰模型的条件下，论文引入了强化学习算法动态优化物联网节点的发射功率、无人机的发射功率和移动轨迹，有效的提高了系统抗干扰性能。	The reinforcement learning algorithm were used to dynamically optimize the transmit of the Internet of Things node and the unmanned aerial vehicle and the moving trajectory about the unmanned aerial vehicle without known the interference model, which can effectively improve the anti-jamming performance of the system.
54298	仿真结果表明，与基于Qlearning的算法相比，基于WoLF-PHC的算法将无人机的效用提升了84. 8%。	The simulation results show that the algorithm-based WoLF-PHC increases the utility of the unmanned aerial vehicle by 84. 8% compared with the algorithm-based Qlearning.
54299	针对多输入多输出(Multiple Input Multiple Output，MIMO)系统中的空时分组码识别(Space-Time Block Code，STBC)问题，本文提出了一种利用卷积-循环神经网络的串行序列空时分组码识别方法。	Aiming at the space-time block code recognition problem of multiple input multiple output system, a method of space-time block code recognition for serial sequences by using convolutional cyclic neural network is proposed.
54300	将一维接收信号的实部和虚部分离后输入网络，利用卷积神经网络(CNN)提取其空间特征，结合循环神经网络(RNN)提取其深层时序特征，提高网络的特征表达能力;	The real and imaginary parts of the one-dimensional received signal are separated into the network, and the spatial characteristics are extracted by the convolutional neural network, and the deep-seated temporal characteristics are extracted by the recurrent neural network, so as to improve the characteristic expression ability of the network.
54301	网络训练过程采用反向传播方法，通过计算输出与目标值的误差，将误差反向传回网络中并更新权值，完成网络的训练过程;	The network training process adopts the back propagation method to calculate the error between the output and the target value, send the error back to the network and update the weight to complete the network training.
54302	将测试集数据输入训练好的网络中，实现对空时分组码的识别和区分。	The test data is input into the trained network to realize the recognition of STBC code.
54303	该方法将深度学习算法运用到串行序列空时分组码识别当中，训练完的网络可直接对单接收天线下的空时分组码进行识别，不需要重复计算信号的统计特征，避免了人为设计特征参数和检测阈值。	This method applies the deep learning algorithm to the recognition of space-time block codes of serial sequences for the first time.
54304	该方法不需要知道信道和噪声的先验信息，适用于电子侦查等非协作通信情况。	The trained network can directly recognize space-time block codes under a single receiving antenna without the need to double calculate the statistical characteristics of the signal and avoid the artificial design of feature parameters and detection threshold.
54305	仿真实验表明，该算法能够有效地对串行序列空时分组码进行识别，并且在低信噪比下有较好的识别性能。	Simulation results show that the algorithm can recognize the space-time block codes of serial sequences and has good recognition performance under low SNR.
54306	为了解决传统的相关滤波跟踪算法在复杂环境中容易跟踪失败的问题，本文提出时间驱动的异常学习相关滤波器，旨在提高模型在复杂环境下的适应性，实现安全有效的目标跟踪。	In order to solve the problem that the traditional correlation filter tracking algorithm is easy to fail in complex environments, this paper proposes time-driven correlation filter with aberrance learning(ALTCF) to improve the adaptability of the model in complex environments and achieve safe and effective object tracking.