ID 原文 译文
22185 假设扩展目标(ET)的扩展和量测数目分别为椭圆和泊松模型，高斯逆威沙特概率假设密度(GIW-PHD)能够估计扩展目标的运动和扩展状态。 Assumed that extension and measurement number of Extended Targets (ET) are respectively modeled as ellipse and Poisson, a Gaussian Inverse Wishart Probability Hypothesis Density (GIW-PHD) filter can estimate kinematic and extension states.
22186 然而，该滤波器对空间邻近目标的数目、非椭圆目标和受到遮挡目标的扩展估计不够准确。 However, for the number of spatially close targets and the extensions of non-ellipsoidal and occluded targets, the results estimated by this filter are not accurate enough.
22187 针对这些问题，该文提出一种改进的 GIW-PHD。 In view of these problems, an improved GIW-PHD filter is proposed in this paper.
22188 首先，假设目标扩展为一个相同尺寸的参考椭圆，通过设计新的散射矩阵得到改进的随机矩阵(RM)方法。 Firstly, assumed that target extension is modeled as a reference ellipse of the same size, a modified Random Matrix (RM) method is obtained by devising a new scatter matrix.
22189 然后，将改进的 RM 方法与假设量测数目服从多伯努利分布的 ET-PHD 结合，得到改进的 GIW-PHD 滤波器。 Then, combining the improved RM method with the ET-PHD based on a measurement number multi-Bernoulli model, the improved GIW-PHD filter is obtained.
22190 仿真和实验结果表明，与传统 GIW-PHD 相比，改进的 GIW- PHD 估计的目标数目和量测数目较多，扩展较大的椭圆和非椭圆目标的扩展更准确。 Simulated and experimental results show that, compared with the traditional GIW-PHD, the improved GIW-PHD filter can obtain more accurate estimates in target number and the extensions of ellipsoidal and non-ellipsoidal targets with large measurement number and extensions.
22191 该文将 T-分布随机近邻嵌入(TSNE)引入到聚类集成问题中，提出一种基于 TSNE 的聚类集成方法。 T-distributed Stochastic Neighbor Embedding (TSNE) is introduced into cluster ensemble problem and a cluster ensemble approach based on TSNE is proposed.
22192 首先通过 TSNE 最小化超图邻接矩阵的行对应的高维数据点与低维映射点分布之间的 KL 散度，使得高维空间结构在低维空间得以保持，然后在低维空间运行层次聚类算法获得最终的聚类结果。 First, TSNE is utilized to minimize Kullback-Leibler divergences between the high-dimensinal points corresponding to the rows of hypergraph's adjacent matrix and the low-dimensional mapping points, which preserves the structure of high-dimensional space in low-dimensional space. Then, a hierarchical clustering algorithm is carried out in the low-dimensional space to obtain the final clustering result.
22193 在基准数据集上的实验结果表明： TSNE 能够提高层次聚类算法的聚类质量，该文方法获得了优于主流聚类集成方法的结果。 Experimental results on several baseline datasets indicate that TSNE can improve the cluster results of hierarchical clustering algorithm and the proposed cluster ensemble method via TSNE outperforms state-of-the-art methods.
22194 针对现有压缩算法通过增加复杂度来降低压缩率，获得信息高效传输的问题。 In order to obtain efficient information transmission, the existing compression algorithms reduce the compression ratio by increasing complexity.