| ID | 原文 | 译文 |
| 54697 | 研究无人飞行器(unmanned aerial vehicle,UAV)在线可飞行航迹的自主规划对UAV适应非结构化环境、提高机动作战能力具有重要的现实意义。 | Studies of unmanned aerial vehicles (unmanned aerial vehicle, UAV) online can flight path planning for UAV autonomous adapt to unstructured environment, improve the ability of mobile warfare has important practical significance. |
| 54698 | 提出了一种基于Pythagorean Hodograph(PH)曲线的UAV在线航迹生成算法,可以根据UAV当前的飞行状态、目标点信息及传感器探测信息实时规划出曲率连续的可避碰飞行航迹。 | Is proposed based on a Pythagorean Hodograph (PH) curve of UAV track online generation algorithm, can be based on the current UAV flight status, target information and sensor to detect real-time planning out the curvature can be collision avoidance flight path. |
| 54699 | 考虑系统动态性能约束,采用分布估计算法对航迹参数进行优化选取,提出基于区间选优的全局精英个体概率选择机制,提高了航迹生成的速度及精度。 | Dynamic performance of the system, optimize the distribution estimation algorithm is adopted to track parameters, the global elite individual selection based on interval probability selection mechanism, improve the speed and precision of the track is generated. |
| 54700 | 根据速度障碍法原理,结合PH曲线的特点,给出了高动态环境下多UAV的实时动态避碰规划算法,该算法能使轨迹快速趋近于目标。 | According to the principle of speed barrier method, combining with the characteristics of PH curve, the high dynamic environment is given more UAV real-time dynamic collision avoidance planning algorithm, this algorithm can fast approaching to the target trajectory. |
| 54701 | 对一组UAV的航迹规划在不同环境下进行了仿真实验,仿真结果证明了算法的有效性和实用性。 | For a set of UAV flight path planning simulation experiment was carried out in different environment, the simulation results prove the validity and practicability of the algorithm. |
| 54702 | 在线多示例目标跟踪算法无法判别目标丢失以及无法适应目标尺度的变化。 | Online sample target tracking algorithm can't distinguish more lost and unable to adapt to changes in the target dimension. |
| 54703 | 提出了一种基于视觉字典的在线多示例目标跟踪算法。 | Put forward a kind of online sample more target tracking algorithm based on visual dictionary. |
| 54704 | 算法将视觉字典和多示例跟踪分别作为检测器和跟踪器,利用互反馈技术提高跟踪性能。 | Algorithm and the visual dictionary more sample tracking as a detector and tracker, respectively using mutual feedback technology to improve the tracking performance. |
| 54705 | 跟踪器完成目标的跟踪并为视觉字典的构建和更新提供训练样本;检测器则对跟踪器的结果(候选样本)进行判定,目标丢失时,暂停跟踪并重新检测目标,目标未丢失时,利用Ransac算法获得目标的尺度变换系数并在新尺度下更新跟踪器。 | Tracking target tracking and provide visual dictionary construction and update the training sample;Detector is the results of the tracker to identify (candidate), the target is lost, the suspension of target tracking and detection, the target is not lost, use Ransac algorithm to obtain the scale of the target transform coefficient and update the tracker in the new scale. |
| 54706 | 为了提高目标丢失判别的准确性,提出了一种局部随机抽样的直方图相似性度量技术,采用局部划分思想和Noisy-NR模型计算候选样本与训练样本特征直方图的相似性,减少了传统直方图匹配由于受目标局部遮挡影响造成的误判。 | In order to improve the accuracy of target loss criterion, this paper proposes a local histogram similarity measure technology of random sampling, the idea of local division and Noisy - NR model candidate sample and training sample feature histogram similarity, reduces the traditional histogram matching due to the effect of target partial shade caused by misjudgment. |