ID 原文 译文
18795 该文着重解决的是汽车终端进行任务卸载时卸载对象的匹配问题。 This paper focuses onmatching the offloaded objects when the car terminal performs the task offloading.
18796 文中引入了软件定义车载网络(SDN-V)对全局变量统一调度，实现了资源控制管理、设备信息采集以及任务信息分析。 By introducing theSoftware-Defined in-Vehicle Network (SDN-V) to schedule uniformly global variables, which realizes resourcecontrol management, device information collection and task information analysis.
18797 基于用户任务的差异化性质，定义了重要度的模型，在此基础上，通过设计任务卸载优先级机制算法，实现任务优先级划分。 Based on the differentiatednature of user tasks, a model of importance is defined. On this basis, task priority is divided by designing thetask to offload the priority mechanism.
18798 针对多目标优化模型，采用乘子法对非凸优化模型进行求解。 For the multi-objective optimization model, the non-convexoptimization model is solved by the multiplier method.
18799 仿真结果表明，与其他卸载策略相比，该文所提卸载机制对时延和能耗优化效果明显，能够最大程度地保证用户的效益。 The simulation results show that compared with otheroffloading strategies, the proposed offloading mechanism has obvious effects on delay and energy consumptionoptimization, which can guarantee the benefit of users to the greatest extent.
18800 机载外辐射源雷达系统中，部署在飞机上的观测站传感器位置无法精确获知，观测站位置误差将严重影响目标定位精度。 An observer is placed on the airborne in the multistatic passive radar localization system. The errorin observer position may seriously affect the localization accuracy.
18801 对此，该文提出一种观测站位置误差下多基外辐射源雷达3维定位代数解算法。 An algebraic closed-form solution is proposed for 3D localization of multistatic passive radar in the presence of sensor position errors.
18802 该算法首先利用辅助变量将非线性双基距离和差(BRD)观测方程进行线性化，构造伪线性目标估计模型。 Firstly, the nonlinearBistatic Range Difference (BRD) measurement equations are linearized by proper additional parameters and apseudo-linear estimation model is given accordingly.
18803 然后将观测站位置量测噪声的统计特性融入定位算法，提出一种改进两步加权最小二乘(TS-WLS)算法实现观测站位置误差下外辐射源雷达目标定位。 Then a modified Two-Step Weighted Least Squares (TS-WLS) algorithm is developed with considering the statistic characteristics of the observer position measurementnoises.
18804 最后推导了克拉美罗下界(CRLB)和算法的理论误差。 Finally the Cramer-Rao Lower Bound (CRLB) and the theoretical error of the algorithm are derived.