ID 原文 译文
23335 机载圆迹 SAR 数据处理结果说明了该方法的有效性。 The proposed method is demonstrated on airborne CSAR moving targets dataset.
23336 该文针对直觉模糊聚类算法不考虑空间邻域信息的缺点，提出一种基于核空间和加权邻域约束的直觉模糊C 均值聚类算法。 To overcome the shortcoming of Intuitionistic Fuzzy C-Means (IFCM) that it does not take into account the spatial information, a new Kernel-based algorithm with Weighted Spatial Information (KWSI_IFCM) is proposed.
23337 该算法首先在直觉模糊 C 均值(Intuitionistic Fuzzy C-Means, IFCM)算法的基础上加入空间邻域约束关系，且赋予邻域内每个点不同的权重； Firstly, the constraint of weighted spatial neighborhood information is added.
23338 接着采用核诱导函数代替欧氏距离计算各点到聚类中心的距离； Secondly, instead of Euclidean distance, kernel-induced function is used to measure the distance between pixels and cluster centers.
23339 然后创建包含邻域信息的新的目标函数，最优化该目标函数得到新的隶属度及聚类中心的迭代表达式。 Thirdly, a new clustering objective function is created and then the iterative expressions of new membership and clustering centers are obtained by optimizing the new function.
23340 利用所提出的新算法与同类聚类算法及基于显著过渡区域的二值化算法进行图像分割，并对结果进行定量分析后可知，所提出的算法最高能够得到 0.9776 的 F 度量值。 The quantitative analysis of image segmentation results using the new algorithm, other similar methods and a binarization method based on salient transition region shows that the new algorithm can get the F-measure value with 0.9776.
23341 实验结果表明新算法性能稳定并且具有较高的分割精度。 The experimental results demonstrate that the proposed algorithm can obtain higher stability and segmentation accuracy than similar fuzzy C-mean algorithm.
23342 针对方位依赖阵列误差的校正问题，通过引入少量精确校正的辅助阵元，该文给出一种基于空域稀疏性的方位依赖阵列误差校正算法。 For calibration of direction-dependent gain-phase errors, with a few precisely calibrated instrumental sensors, a method that jointly estimates the direction-dependent gain-phase errors and the target azimuth by spatial sparsity of the signal is proposed.
23343 将受方位依赖阵列误差扰动的阵列流型表示为理想情况下的阵列流型与幅相误差系数矩阵的乘积形式。 The array manifold that perturbed by direction-dependent gain-phase errors is denoted by the multiplication form of ideally array manifold and a gain-phase errors coefficient matrix, then the received signal is represented by sparse form.
23344 同时利用接收信号的空域稀疏性，对接收信号进行稀疏表示，将阵列误差自校正问题转化为一个二元最优化问题，再通过交替迭代的优化方式求得两个优化变量的最优解，从而实现了信号方位与方位依赖阵列误差的联合估计。 The calibration for gain-phase error problem is formulated as a dual optimization problem, through alternating iterative optimization method to acquire the optimal solution of the two optimization variables, so as to realize the signal incident angle and azimuth dependent amplitude and phase errors of the optimized calculation.