ID 原文 译文
53847 研究了具有三个全双工源节点的协作通信系统，为提升系统有效性和可靠性，提出了两种信源间全双工协作通信的空时编码方案。 The cooperative communication system with three full duplex source nodes was studied. In order to improve the effectiveness and reliability of the system, two space-time coding schemes for full duplex cooperative communication between two sources were proposed.
53848 将正交空时码理论应用其中，可以有效利用空间分集增益，抵抗多径衰落，提高系统性能。 The orthogonal space-time coding theory was applied to improve the performance of the system by effectively utilizing the spatial diversity gain, resisting multipath fading.
53849 方案分为共享兼传输阶段与协作传输阶段。 The scheme was divided into sharing and transmission phase and cooperative transmission phase.
53850 在共享兼传输阶段进行信源间的信息共享同时也会向目的节点发送信息，在协作传输阶段，源节点间形成虚拟MIMO向目的节点发送信息。 In the sharing and transmission phase, the information was shared between the sources and sent to the destination node. In the cooperative transmission phase, virtual MIMO was formed between the source nodes to send information to the destination node.
53851 推导了系统各个接收端的中断概率， The outage probability of each receiver was deduced.
53852 仿真结果表明，与现有源节点工作在半双工模式下的协作传输方案对比，该方案有效提升了吞吐量和中断概率性能， The simulation results show that, compared with the traditional source node transmission scheme working in half duplex mode, the scheme can effectively improve the throughput and the outage probability performance.
53853 在中断概率为10-3时，该方案有1.5 dB左右的信噪比增益，与无协作传输对比，该方案有约8 dB左右的信噪比增益。 At the same time, when the outage probability is 10-3, the scheme has a signal-to-noise ratio gain of about 1. 5 dB, and compared with the non-cooperative transmission, the scheme has a signal-to-noise ratio gain of about 8 dB.
53854 传统相关滤波跟踪算法试图引入预定义的正则项，如抑制背景学习或限制相关滤波器的学习率来提高算法的鲁棒性， Traditional correlation filter tracking algorithms try to introduce predefined regularization terms, such as restraining background learning or limiting the learning rate of correlation filter, to improve the robustness of the algorithm.
53855 但在复杂场景下还是容易发生目标跟踪丢失，因为传统相关滤波跟踪算法没有关注相邻两帧之间的信息变化。 However, in complex scenes, target tracking loss is easy to occur, because the traditional correlation filter tracking algorithm does not pay attention to the information changes between two adjacent frames.
53856 针对以上问题，本文提出专注学习时空关系的相关滤波跟踪算法，引入相邻两帧的响应图变化作为空间正则项权值的参考权重，而当前帧的响应图的振荡程度确定时间正则项权值，最后本文通过交替方向乘子法(ADMM)迭代优化本文的损失函数。 In order to solve the above problems, this paper proposes a correlation filter tracking algorithm focusing on learning the spatial-temporal relationship. The change of the response graph of two adjacent frames is introduced as the reference weight of the weight value of the spatial regularization term, and the oscillation degree of the response graph of the current frame determines the weight value of the temporal regularization term.