ID 原文 译文
16745 针对一般跟踪算法不能很好地解决航拍视频下目标分辨率低、视场大、视角变化多等特殊难点，该文提出一种融合目标显著性和在线学习干扰因子的无人机(UAV)跟踪算法。 In view of the fact that the general tracking algorithm can not solve the special problems such as lowresolution, large field of view and many changes of view angle, a Unmanned Aerial Vehicle (UAV) trackingalgorithm combining target saliency and online learning interference factor is proposed.
16746 通用模型预训练的深层特征无法有效地识别航拍目标，该文跟踪算法能根据反向传播梯度识别每个卷积滤波器的重要性来更好地选择目标显著性特征，以此凸显航拍目标特性。 The deep feature that the general model pre-trained can not effectively identify the aerial target, the tracking algorithm can better select the salient feature of each convolution filter according to the importance of the back propagation gradient, so as to highlight the aerial target feature.
16747 另外充分利用连续视频丰富的上下文信息，通过引导目标外观模型与当前帧尽可能相似地来在线学习动态目标的干扰因子，从而实现可靠的自适应匹配跟踪。 In addition, it makes full use of the rich contextinformation of the continuous video, and learn the interference factor of the dynamic target online by guidingthe target appearance model as similar as possible to the current frame, so as to achieve reliable adaptivematching tracking.
16748 实验证明：该算法在跟踪难点更多的UAV123数据集上跟踪成功率和准确率分别比孪生网络基准算法高5.3%和3.6%，同时速度达到平均28.7帧/s，基本满足航拍目标跟踪准确性和实时性需求。 It is proved that the tracking success rate and accuracy rate of the algorithm are 5.3% and3.6% higher than that of the siamese network benchmark algorithm on the more difficult UAV123 dataset,respectively, and the speed reaches an average of 28.7 frames per second, which basically meet the aerial targettracking accuracy and real-time requirements.
16749 针对源节点和中继节点均采用收集能量供电的放大转发中继网络，考虑两个目的节点之间信息相互保密的场景，该文提出最大化长期时间平均保密速率的源节点和中继节点发送功率联合优化算法。 For amplify-and-forward relay networks where both the source node and the relay node are poweredby the harvested energy and the information for the two destination nodes are required to keep secrecy eachother, an algorithm is proposed to maximize the long-term average secrecy rate by jointly optimizing thetransmission power of the source node and the relay node.
16750 由于能量到达和信道状态是随机过程，该问题是一个随机优化问题。 Since the energy arrivals and channel states arestochastic processes, the problem is a stochastic optimization problem.
16751 利用Lyapunov优化框架将电池操作和能量使用约束下的长期优化问题转化为每时隙的“虚队列漂移加惩罚”最小化问题，并求解。 The Lyapunov optimization frameworkis used to transform the long-term optimization problem into a “virtual queue drift plus penalty”minimization problem per time slot under the constraints of battery operation and energy using. The transformed optimization problem is solved.
16752 仿真结果显示该文提出的算法在长期平均保密速率上相较于对比算法具有显著的优势，同时算法仅依赖于当前的电池状态和信道状态信息做出决策，是一种实用的、低复杂度的算法。 The simulation results show that the proposed algorithm has significant advantages over the comparison algorithms in the long-term average secrecy rate. Furthermore, theproposed algorithm only depends on the current battery state and channel state to make the decision, which isa practical and low-complexity algorithm.
16753 雷达对抗的核心研究内容主要是干扰策略与抗干扰策略之间的对抗博弈，其作为电子战研究领域的热点一直备受学者们关注。 The core research contents of radar countermeasures are the games of countermeasures betweenjamming strategies and anti-jamming strategies. As a hotspot in the field of electronic warfare, radarcountermeasures have been paid much attention by scholars.
16754 该文综述了学者们利用合作与非合作博弈方法来分析雷达在进行目标探测和干扰抑制时所使用的策略， This paper summarizes that the scholars employ the cooperative and non-cooperative game methods to analyze the radar against jamming while probing targets.