ID 原文 译文
40776 在以到达角(AoA)和到达时间(ToA)为观测量的三维目标跟踪中，异常值导致非线性滤波性能明显下降甚至发散。 In the three-dimensional target tracking with angle of arrival(AoA) and time of arrival(ToA) measurements, the outliers lead to significant performance degradation or even divergence of nonlinear filter.
40777 针对该问题，提出了一种基于M估计的鲁棒偏差补偿卡尔曼滤波算法(MR-BCKF)。 Aiming at this problem, an M-estimation-based robust bias compensation Kalman filter(MR-BCKF) algorithm is proposed in this paper.
40778 该算法首先利用AoA和ToA的等价几何关系对非线性观测方程进行伪线性化，接着依据M估计准则推导鲁棒伪线性卡尔曼滤波，然后采用偏差补偿策略提高跟踪精度。 The algorithm pseudo-linearizes the nonlinear measurement equations through the equivalent geometric relationship between AoA and ToA, and then derives the robust pseudo-linear Kalman filter by exploiting the M estimation criterion, followed by the bias compensation to improve the tracking accuracy.
40779 MR-BCKF利用马氏距离判别异常值，不依赖于噪声统计特性，并且通过构建改进的三段式权重函数增强鲁棒性。 Moreover, the MR-BCKF uses Mahalanobis distance to distinguish outliers, which does not depend on the noise statistics, and enhances the robustness by constructing an improved three-segment weight function.
40780 仿真结果表明，MR-BCKF相较于其他鲁棒滤波算法不仅能提高孤立型异常值的抑制效果，而且在连续型异常值情况下也取得更高的跟踪精度。 Simulation results shows that compared with other robust Kalman filter, the MR-BCKF can not only improve the suppression effect of isolated outliers, but also achieve higher tracking accuracy in the case of continuous outliers.
40781 针对传统的结构图提取方法难以在纹理平滑的同时更好的保持结构这一问题，采用迭代重加权最小二乘优化框架构建优化模型。 Aiming at the problem that the traditional structure extraction method is difficult to maintain the structure while smoothing the texture, the iterative reweighted least squares optimization framework is used to construct an optimization model.
40782 针对图像中纹理与结构的梯度的不同，提出了相对极值变分。 In view of the difference in the gradient of texture and structure in the image, a relative extremum variation is proposed.
40783 首先通过形态学中的闭合运算提取梯度图的极值图，然后扩展了窗口内在变分的指数权重，进而构建了新的优化模型。 Firstly, the extremum information of the gradient map is extracted through closed operation in morphology.And then, the index weight of windowed inherent variation is expanded, and then a new optimization model is constructed.
40784 最后根据模型的非凸性，构建了Toeplitz矩阵进行数值求解。 Finally, according to the non-convexity of the model, a Toeplitz matrix is constructed for numerical solution.
40785 实验结果表明所提算法可以有效的移除图像纹理，更好的保持边缘结构与对比度，在图像质量分析方面也展现了较好的优势。 The experimental results show that the proposed algorithm can effectively remove the image texture, better preserve the edge structure and contrast, and also shows better advantages in image quality analysis.