ID 原文 译文
20615 该文针对星基定位接收机导航信号捕获的干扰问题，提出一种非完备空际间叠干扰信号模型。 A non-complete spatial overlapped interference signal model is proposed based on the jamming research against satellite-based positioning receiver for the acquisition of navigation signal.
20616 首先对提出的干扰模型以及非完备空际间叠引起的干扰裂变效应进行了阐述分析与推究证明，而后推导计算出了星基定位接收机输出信干噪比(SINR)与空际间叠长度的函数式，论证了两者函数单调性关系。 Firstly, the interference model is introduced and analyzed, and the signal fission effect induced by non-complete spatial overlapped interference is demonstrated. Then, the relationship between SINR of satellite-based positioningreceiver and spatial overlapped length is derived, and the monotonic relationship of them is deduced.
20617 仿真实验表明星基定位接收机输出信干噪比为空际间叠长度的单调增函数，短空际间叠长度干扰可抑制3维频码域相关峰突起，降消星基定位接收机捕获性能。 Simulation results suggest that SINR of satellite-based positioning receiver is the monotonically increasing function of spatial overlapped length, and the short-spatial-overlapped interference can restrain the peak amplitude of three dimensional frequency and coded domain correlation, degrading the performance of acquisition of satellite-based positioning receiver.
20618 针对光照变化引起目标跟踪性能显著下降的问题，该文提出一种联合优化光照补偿和多任务逆向稀疏表示的视觉跟踪方法。 Focusing on the issue of heavy decrease of object tracking performance induced by illumination variation, a visual tracking method via jointly optimizing the illumination compensation and multi-task reverse sparse representation is proposed.
20619 首先基于模板与候选目标的平均亮度差异对模板实施光照补偿，并利用候选目标逆向稀疏表示光照补偿后的模板。 The template illumination is firstly compensated by the developed algorithm, which is based on the average brightness difference between templates and candidates. In what follows, the candidate set is exploited to sparsely represent the templates after illumination compensation.
20620 而后将所得多个关于单模板的优化问题转化为一个关于多模板的多任务优化问题，并利用交替迭代方法求解此多任务优化问题以获得最优光照补偿系数矩阵以及稀疏编码矩阵。 Subsequently,the obtained multiple optimization issues associated with single template can be recast as a multi-taskoptimization one related to multiple templates, which can be solved by the alternative iteration approach toacquire the optimal illumination compensation coefficient and the sparse coding matrix.
20621 最后利用所得稀疏编码矩阵快速剔除无关候选目标，并采用局部结构化评估方法实现目标精确跟踪。 Finally, the obtainedsparse coding matrix can be exploited to quickly eliminate the unrelated candidates, afterwards the localstructured evaluation method is employed to achieve the accurate object tracking.
20622 仿真结果表明，与现有主流算法相比，剧烈光照变化情况下，所提方法可显著改善目标跟踪精度及稳健性。 As compared to the existingstate-of-the-art algorithms, simulation results show that the proposed algorithm can improve the accuracy androbustness of the object tracking significantly in the presence of heavy illumination variation.
20623 传统基于特征的景象匹配方法存在冗余点多、匹配精度低等问题，难以同时满足实时性及鲁棒性要求，对此，论文提出一种基于尺度不变特征变换(SIFT)的快速景象匹配方法。 The traditional feature-based image matching method has many problems such as many redundant points and low matching accuracy, which can hardly meet the real-time and robustness requirements. In this regard, a fast scene matching method based on Scale Invariant Feature Transform (SIFT) is proposed.
20624 在特征提取阶段，采用高速分段特征检测器(FAST)在多尺度检测角点作为初始特征，经过高斯差分(DOG)算子在尺度空间中进行特征的2次筛选，简化原有遍历式的特征搜索过程； In the feature detection phase, FAST (Features from Accelerated Segment Test) is used to detect characteristics in multi-scale, after then, combining with Difference Of Gauss (DOG) operators to filter characteristics again. From this, the feature search process is simplified.