基于界面跟踪方法的汽蚀模型和算法的有效性验证

针对两相附着汽蚀流动机理,基于界面跟踪方法发展了新的汽蚀模型和算法。所发展的汽蚀模型和算法不仅考虑了液相／气相界面处的压力差,而且考虑了耦合Reynolds-Averaged Navier-Stokes方程求解技术得到的流场压力梯度信息来迭代计算附着汽蚀形状。采用具有试验数据的半球形头部圆柱体汽蚀绕流作为算例来验证所提出的汽蚀模型和算法的有效性。采用不同的网格数和松弛因子数值验证了发展的汽蚀模型和算法的有效性。三种汽蚀数下的数值计算结果得到的压力系数分布与试验数据完全吻合。结果表明所提出的汽蚀模型和算法能够准确模拟出汽蚀发生点和汽蚀长度。     

椭圆法用于阳极溶出伏安法测定微量银

用新物理量Ｖｏｐ的椭圆法对阳极溶出伏安法分析的含银离子浓度为１０＾５－１０＾－９ｍｏｌ／Ｌ的一系列溶液进行了研究。结果表明：光学方法与电化学方法所得分析结果相同；椭圆法可检测的浓度下限比电化学方法低一个数量级以上。而且测量的相对平均偏差也小于电化学方法。     

基于GNSS多通道跟踪接收机的阵列反欺骗方法

近年来,基于阵列天线的空域处理方法被认为是最有效的欺骗检测和抑制手段之一。提出了一种基于卫星导航接收机多通道跟踪能力的阵列反欺骗方法。多通道跟踪接收机对每一个捕获到的导航信号在所有阵元通道后均进行跟踪处理并提取信号幅度和载波相位观测量,首先计算载波相位双差平方和进行欺骗检测,然后利用载波相位单差和信号幅度比估计信号导引矢量,从而进行基于子空间投影的欺骗信号抑制或基于波束形成的真实卫星信噪比提升。理论分析和仿真试验证明,该方法可有效检测并消除同一来向的欺骗信号且不影响真实卫星信号质量。由于前端非理想因素带来的幅度和相位偏移都已包含在接收机跟踪通道提取的观测量内,实际应用时对阵列天线校准和射频通道均衡的要求明显降低。     

基于背景感知的相关滤波抗遮挡优化算法

针对相关滤波目标跟踪中由于目标遮挡、出视野引起的跟踪失败等问题,以背景感知相关滤波(background aware correlation filters, BACF)算法为基础,提出一种抗遮挡优化算法。首先特征提取部分融合方向梯度直方图(histogram of oriented gradient, HOG)特征与颜色名称(color name, CN)特征;其次提出层级遮挡优化算法,在目标跟踪过程中根据平均峰值相关能量(average peak to correlation energy,APCE)和最大响应峰值判断目标外观是否发生较大变化,并通过巴氏距离进一步计算目标相邻滤波器模板特征相似程度,以此判断造成目标外观变化的真正原因,进而决定是否更新模板;针对目标出视野问题,提出模板筛查策略,跟踪过程中每隔K帧对滤波器模板进行置信度考查,若目标丢失,则生成新的模板并对目标进行全局搜索重新抓取目标。通过在目标跟踪数据集OTB2015上测试,优化算法精确度与成功率分别为83.0%与78.8%,有效提高了算法性能。     

Real-time 3D multi-pedestrian detection and tracking using 3D LiDAR point cloud for mobile robot

Mobile robots are used in modern life; however, object recognition is still insufficient to realize robot navigation in crowded environments. Mobile robots must rapidly and accurately recognize the movements and shapes of pedestrians to navigate safely in pedestrian-rich spaces. This study proposes real-time, accurate, three-dimensional (3D) multi-pedestrian detection and tracking using a 3D light detection and ranging (LiDAR) point cloud in crowded environments. The pedestrian detection quickly segments a sparse 3D point cloud into individual pedestrians using a lightweight convolutional autoencoder and connected-component algorithm. The multi-pedestrian tracking identifies the same pedestrians considering motion and appearance cues in continuing frames. In addition, it estimates pedestrians' dynamic movements with various patterns by adaptively mixing heterogeneous motion models. We evaluate the computational speed and accuracy of each module using the KITTI dataset. We demonstrate that our integrated system, which rapidly and accurately recognizes pedestrian movement and appearance using a sparse 3D LiDAR, is applicable for robot navigation in crowded spaces.     

扩频体制低轨卫星通信信号捕获与跟踪系统设计

针对扩频体制下低轨卫星信号的捕获及跟踪等系统设计实现问题,根据卫星信号模型进行了捕获及跟踪算法设计,重点对基于快速傅里叶变换的快速捕获算法、锁频环、锁相环和码跟踪环路进行了设计,并进行了工程系统实现。试验验证表明,该系统可以实现码分多址体制低轨卫星信号捕获、跟踪处理,工作性能参数满足系统需求。     

面向多波束卫星通信网络的步进跟踪方法

面向多波束覆盖的卫星通信网络,为解决相控阵用户终端位置姿态信息无法获取时程序跟踪卫星失效的问题,提出了一种步进扫描跟踪方法。在该方法中,终端仅需比较单个波束的信标接收能量即可判断波束指向,从而降低跟踪时延。在此基础上,为满足终端在动中通场景下的业务通信需求,进一步研究动态条件下的跟踪方法,并讨论跟踪时延的影响因素。实验结果表明,当终端位置姿态变化较小时,所提方法的跟踪性能接近程序跟踪。     

Event-triggered type-2 fuzzy-based sliding mode control for steer-by-wire systems

This paper proposes an event-triggered higher-order sliding mode control for steer-by-wire (SbW) systems subject to limited communication resources and uncertain nonlinearity. First, an interval type-2 fuzzy logic system (IT2 FLS) is adopted to approximate the uncertain nonlinearities. A fuzzy-based state observer is developed to estimate unavailable states of the extended SbW system. Then, to save communication resources and eliminate chattering, an event-triggered higher-order sliding mode control is proposed for the SbW system. The key advantage is that the proposed control scheme can offset the observation error and the event-triggering error. After that, the practical finite-time stability of the closed-loop SbW system is proved in the framework of the Lyapunov theory. Finally, numerical simulations and vehicle experiments are given to evaluate the effectiveness and superiority of the proposed scheme.     

视场非完全重叠的分布式雷达多目标跟踪方法

在探测能力、波形设计及天线指向等因素制约下,分布式雷达视场并非完全重合,由此造成的观测信息差异给后续信息融合带来了巨大挑战。该文基于高斯混合实现的集势概率假设密度(CPHD)滤波器,提出了一种视场部分重叠下的分布式雷达多目标跟踪方法。首先,利用多目标密度乘积切分出概率假设密度(PHD)中表征共同观测信息的部分;之后,标准的分布式融合(算术平均或几何平均融合)方法作用于切分出的共同观测目标信息以提升跟踪性能,补偿融合则作用于雷达单独观测目标信息以扩展视场范围。该文方法无须视场先验信息,能够适应雷达视场未知时的分布式融合多目标跟踪场景。仿真实验验证了所提出方法在未知、时变雷达视场下跟踪多目标的性能,表明了该文方法比基于高斯混合的聚类方法性能更好。      

RFID-based 3D human pose tracking: A subject generalization approach

Three-dimensional (3D) human pose tracking has recently attracted more and more attention in the computer vision field. Real-time pose tracking is highly useful in various domains such as video surveillance, somatosensory games, and human-computer interaction. However, vision-based pose tracking techniques usually raise privacy concerns, making human pose tracking without vision data usage an important problem. Thus, we propose using Radio Frequency Identification (RFID) as a pose tracking technique via a low-cost wearable sensing device. Although our prior work illustrated how deep learning could transfer RFID data into real-time human poses, generalization for different subjects remains challenging. This paper proposes a subject-adaptive technique to address this generalization problem. In the proposed system, termed Cycle-Pose, we leverage a cross-skeleton learning structure to improve the adaptability of the deep learning model to different human skeletons. Moreover, our novel cycle kinematic network is proposed for unpaired RFID and labeled pose data from different subjects. The Cycle-Pose system is implemented and evaluated by comparing its prototype with a traditional RFID pose tracking system. The experimental results demonstrate that Cycle-Pose can achieve lower estimation error and better subject generalization than the traditional system.