ID 原文 译文
23245 通过电磁计算数据和暗室测量数据验证了所提方法的有效性和稳定性。 The method can estimate stably the 2D geometric feature of midcourse targets during the midcourse flight, which is verified by the simulation experiments with electromagnetic computed data and measured data in anechoic chamber.
23246 在雷达等高速移动的通信系统中，由于传输过程中的时延和多普勒频移，在分析跳频序列的性能时，需要对其时频 2 维汉明相关性进行分析。 In the high-speed mobile communication system such as the radar, due to time delay and Doppler shift in the transmission process, it is needed to analyze Time-Frequency (TF) two-dimensional (2-D) Hamming correlation of the Frequency Hopping Sequence (FHS).
23247 线性移位寄存器序列(m-序列)具有良好的随机、平衡等性质，因此 m-序列已被广泛应用到跳频序列的构造中。 Linear feedback shift register sequence (m-sequence) has good random and balance properties, so it is widely used to the construction of FHSs.
23248 该文对基于 m-序列的跳频序列集的时频 2 维汉明相关性进行分析，计算了其时频 2 维汉明相关值的分布；构造了具有新参数的跳频序列集。 In this paper, the TF 2-D Hamming correlation of FHS set constructed by m-sequence is analyzed, the distribution of its TF 2-D Hamming correlation is calculated, and an FHS set with new parameters is constructed.
23249 在相同多普勒频移下，新序列集的 2 维相关性与已有基于 m-序列的跳频序列集的 2 维相关性相比较更稳定。 Under the same Doppler shift, the 2-D correlation of the new sequence set is more stable than the 2-D correlation of the existing ones.
23250 该文将 TL1范数应用于子空间聚类的研究中，提出基于 TL1范数约束的子空间聚类优化模型。 The TL1 norm is applied to propose a new optimization model for the study of subspace clustering.
23251 尽管该优化模型是非凸的，在无噪音的情形下，证明了它的最优解为具有块对角结构的系数矩阵，这对随后进行的谱聚类提供了理论保证； Although the optimization is nonconvex, in the case of non-noise, it proves that the optimal solution of the proposed model is the coefficient matrix with block-diagonal structure, which provides the theoretical guarantee for the latter spectral clustering.
23252 在有噪声的情形下，它的约束条件等价于以干净数据为字典的优化模型，因而求解出的系数矩阵提高了聚类的精确度。 In the case of dealing with noise, the constraint condition of this model is presented to be equivalent with the optimal model using the corrected data as the dictionary, which contributes to improving the clustering accuracy.
23253 进一步，利用增广拉格朗日-交替方向乘子方法给出该优化模型的求解方法。 Then, the alternating direction method of Lagrangian multipliers is applied to solving the unknown matrices.
23254 实验结果表明，基于 TL1范数的子空间聚类方法不仅增强了系数矩阵的稀疏性，而且在聚类精确度，对噪音的鲁棒性方面要优于低秩子空间聚类方法和稀疏子空间聚类方法。 Experimental results show that subspace clustering method based on TL1 norm not only enhances the sparsity of coefficient matrix, but also is superior to low-rank subspace clustering and sparse subspace clustering method in terms of clustering accuracy and robustness to noise.