ID 原文 译文
25955 此外，文章探讨了 ID-AOFE 方案的消息交互模型，就争端解决的方案和过程进行了重点分析。 In addition, this paper discussed the message interaction model of the ID-AOFE scheme, and analyzed the mechanism of dispute resolution．
25956 针对 Wi-Fi 无源目标跟踪技术中，由于直射路径信号以及噪声等影响造成提取目标反射路径信号困难等难点，本文提出了基于 Wi-Fi 多维参数特征的无源目标跟踪技术。 In the Wi-Fi passive target tracking technology, it is difficult to extract the target's reflected path signals due to direct path signals and noise effects. A passive target tracking technology based on Wi-Fi multi-dimensional parameter feature is proposed．
25957 该技术采用串行干扰消除代替全零初始化来完成某时刻多条路径到达角(Arrival of Angle，AoA)、飞行时间(Time of Flight，ToF)以及多普勒频移(Doppler FrequencyShifts，DFS)的初始化，并且对传统频域空间交替广义期望最大化(Frequency Domain Space Alternating Generalized Ex-pectation-maximization，FD-SAGE)算法进行改进，弥补了传统算法收敛速度慢以及噪声影响等缺陷。 This technology uses serial interference cancellation instead of all-zero initialization to complete multiple paths initialization including Arrival of Angle (AoA), Time of Flight(ToF)and Doppler Frequency Shifts(DFS). And the Frequency Domain Space Alternating Generalized Expectation-maximization (FD-SAGE) algorithm is improved to make up for traditional algorithms shortcomings such as slow convergence speed and noise impact．
25958 除此之外，本文采用基于最小代价多路径网络的混合数据关联方法解决了在不同时刻具有不同路径数目时无法进行路径有效关联的问题，同时该方法将固定时间窗中的最优匹配作为某时刻的关联数据，避免了某次关联错误导致后续关联失败所造成的不可逆错误。 In addition, a hybrid data association method based on the least-cost multi-path network is used to solve the problem of unable to effectively associate paths when there are different numbers of paths at different times. Meanwhile, the method uses the optimal match in a fixed time window as the correlation data at a certain time, which reduces the irreversible error caused by a subsequent association failure caused by the association error．
25959 实验结果表明，本文在复杂室内环境下能够达到 1. 3m 的平均跟踪定位精度。 Experimental results show that this paper can achieve an average tracking accuracy of 1.3m in a complex indoor environment．
25960 基于松弛支配的高维多目标进化算法(Many-objective Evolutionary Algorithms，MaOEAs)由于能够有效地提高区分解的能力，受到广泛关注，但该类大多数算法处理不同目标的优化问题时普适性较差。 Ｒelaxed dominance relation based evolutionary algorithms for many-objective optimization (MaOEAs) are widely concerned because they effectively improve the ability to identify solutions, however, the most of these algorithms show poor versatility in solving different objectives of optimization problems．
25961 针对这个问题，本文提出一种基于自适应支配准则的高维多目标进化算法(Adaptive Dominance Criterion Based Evolutionary Algorithm forMany-objective Optimization，ADCEA)。 To address this issue, this paper proposes an adaptive dominance criterion based evolutionary algorithm for many-objective optimization(ADCEA)．
25962 首先，自适应准则(Adaptive Dominance Criterion，ADC)根据目标空间中相邻解间的角度信息和目标数目，设计一种自适应小生境方法，并结合收敛性指标信息，实现对候选解的非支配排序。 Firstly, the ADC designs an adaptive niche method based on the angle information and the number of objectives between adjacent solutions in the objective space, and combines the convergence indicator information to achieve non-dominated sorting of candidate solutions．
25963 然后，为了进一步增强种群的多样性，在环境选择中引入参考向量分割种群技术; Then, in order to further enhance the diversity of the population, the reference vector population technique is introduced in the environment selection．
25964 最后，构建合理的适应度函数，并根据适应度值大小选取收敛性和多样性较好的非支配解集。 Finally, a reasonable fitness function is constructed, and the non-dominated solution set with better convergence and diversity is selected according to the fitness value．