基于权值分配及多特征表示的在线多示例学习跟踪

针对复杂环境下目标跟踪过程中由于遮挡、目标姿势及光照条件变化引起跟踪漂移的问题,提出一种基于多示例学习（MIL）框架的在线视觉目标跟踪算法。该算法针对多示例跟踪算法采用单一haar-like特征不能准确描述目标外观变化及在学习过程中对样本包中各正负样本示例采用相同权值,忽略不同正负样本示例在学习过程中对包的重要性不同的特点,采用多特征联合表示目标外观构造分类器,通过将多特征互补特性融入在线多示例学习过程中,利用多特征的互补属性建立准确的目标外观模型,克服在线多示例跟踪算法对目标外观变化描述不足的问题;同时,依据不同正负样本示例对样本包的重要程度进行权值分配,提高跟踪精度。实验结果表明,本文跟踪算法对场景光线剧烈变化、遮挡、尺度变化及平面旋转等干扰具有较强的跟踪鲁棒性,通过对不同视频序列进行测试,文中算法在5组测试视频序列上的平均中心位置误差远小于对比增量式学习跟踪,仅为10.14像素,其对比算法IVT、MIL和OAB的中心位置误差分别为17.99、20.29和33.64像素。      

无人机用燃料电池阴极供气系统建模与控制

燃料电池因其高效、无污染、噪声小等特点,被认为是未来最具有潜力的无人机（UAV）用动力源,燃料电池阴极供气系统的控制技术是决定燃料电池系统性能和可靠性的关键。针对无人机用质子交换膜燃料电池（PEMFC）阴极供气系统,首先,考虑外界温度、压力、空气密度以及雷诺数等随高度变化的参数,建立了跨高度离心空压机模型并分析了其在不同高度下的工作特性,基于无刷直流电机反电势特征构建了高速空压机驱动电机模型。其次,通过计算燃料电池阴极氧气和氮气的动态分压获取了PEMFC电堆输出电压。设计了基于分数阶PI^λD^μ的过氧比和阴极气压控制方法,驱动电机采用有限集模型预测控制（MPC）实现快速的转矩响应,仿真结果表明设计的控制器可在无人机跨高度运行条件下实现过氧比的快速调节,同时维持阴极气压稳定,满足燃料电池阴极供气需求。     

高保真气动伺服弹性系统降阶技术研究

采用高保真非定常气动力辨识技术获得的气动伺服弹性(ASE)系统时域模型往往阶次较高,不利于控制系统设计的应用.为了有效降低高阶ASE系统阶次,研究了高保真ASE模型的降阶特性,分别采用平衡截断、奇异摄动以及平衡奇异摄动模型降阶技术,结合BACT系统讨论了各种方法在ASE模型降阶中的应用情况,通过仿真比较得知,平衡奇异摄动法应用于ASE模型的降阶具有一定的优势,可以大幅降低模型阶次,且能够保证降阶模型的精度.     

Particle flters for probability hypothesis density flter with the presence of unknown measurement noise covariance

In Bayesian multi-target fltering,knowledge of measurement noise variance is very important.Signifcant mismatches in noise parameters will result in biased estimates.In this paper,a new particle flter for a probability hypothesis density（PHD）flter handling unknown measurement noise variances is proposed.The approach is based on marginalizing the unknown parameters out of the posterior distribution by using variational Bayesian（VB）methods.Moreover,the sequential Monte Carlo method is used to approximate the posterior intensity considering non-linear and non-Gaussian conditions.Unlike other particle flters for this challenging class of PHD flters,the proposed method can adaptively learn the unknown and time-varying noise variances while fltering.Simulation results show that the proposed method improves estimation accuracy in terms of both the number of targets and their states.     

Demonstration of a high sensitivity GNSS software receiver for indoor positioning

Advances in signal processing techniques contributed to the significant improvements of GNSS receiver performance in dense multipath environments and created the opportunities for a new category of high-sensitivity GNSS (HS-GNSS) receivers that can provide GNSS location services in indoor environments. The difficulties in improving the availability, reliability, and accuracy of these indoor capable GNSS receivers exceed those of the receivers designed for the most hostile urban canyon environments. The authors of this paper identified the vector tracking schemes, signal propagation statistics, and parallel processing techniques that are critical to a robust HS-GNSS receiver for indoor environments and successfully incorporated them into a fully functional high-sensitivity software receiver. A flexible vector-based receiver architecture is introduced to combine these key indoor signal processing technologies into GSNRx-hs™ – the high sensitivity software navigation receiver developed at the University of Calgary. The resulting receiver can perform multi-mode vector tracking in indoor environment at various levels of location and timing uncertainties. In addition to the obvious improvements in time-to-first-fix (TTFF) and signal sensitivity, the field test results in indoor environments surrounded by wood, glass, and concrete showed that the new techniques effectively improved the performance of indoor GNSS positioning. With fine GNSS timing, the proposed receiver can consistently deliver indoor navigation solution with the horizontal accuracy of 2–15 m depending on the satellite geometry and the indoor environments. If only the coarse GNSS timing is available, the horizontal accuracy of the indoor navigation solution from the proposed receiver is around 30 m depending on the coarse timing accuracy, the satellite geometry, and the indoor environments. From the preliminary field test results, it has been observed that the signal processing sensitivity is the dominant factor on the availability of the indoor navigation solution, while the GNSS timing accuracy is the dominant factor on the accuracy of the indoor navigation solution.     

基于鲁棒自适应反步法的航天器姿态跟踪控制

针对存在未知惯量矩阵和外干扰的刚体航天器姿态跟踪,提出了一种鲁棒自适应控制方法。综合自适应反步法和非线性L2增益干扰抑制方法,用自适应反步法构造系统的Lyapunov函数,获得了具L2增益的鲁棒自适应控制器,以保证姿态跟踪误差系统为一致最终有界稳定,确保估计的航天器惯量参数的有界性,并使从外干扰输入到评价输出的L2增益不大于给定值。仿真结果验证了方法的有效性和可行性。     

GNSS接收机数字载波跟踪环建模与噪声性能分析

针对GNSS接收机高灵敏度跟踪问题,研究数字载波跟踪环热噪声相位颤动与相干积累时间长度的关系.在分析I·Q鉴相器的Costas载波环信号处理流程的基础上,建立了新的环路线性化模型.基于该模型,推导了由最佳模拟环路滤波器双线性变换离散化得到的数字载波跟踪环的噪声性能.数值分析表明:采用I·Q鉴相器的二阶、三阶数字载波环热噪声相位颤动,随相干积累时间的增大先减小后增大,存在热噪声相位颤动最小意义上的最佳相干积累时间.仿真结果验证了理论分析结论,同时表明新建模型对数字载波跟踪环的描述比传统模型更准确.文中给出的各载噪比和环路带宽组合条件下的最佳相干积累时间,可用于指导高灵敏度接收机载波跟踪环设计.     

运载器末助推段非线性姿态控制器设计

根据单摆模型近似晃动质量推导晃动运动与姿态运动的动力学方程,基于状态相关黎卡提方程方法,综合动态逆控制方法设计了运载器末助推段非线性姿态控制器,由状态反馈实现姿态跟踪和晃动抑制。因实际中模拟晃动运动的单摆状态无法测量,引入输出多采样率技术,通过在一个采样周期内多次检测被控对象的输出估计状态,实现设计的状态反馈控制律。数...     

基于梯度方向二值模式特征的飞机翼尖跟踪技术

研究基于视频图像分析与定位的飞机翼尖跟踪技术,为飞机地面移动提供安全保障.现有图像特征进行翼尖跟踪经常失效且计算效率低下,本文结合纹理和轮廓信息,提出以梯度方向二值模式( OG_ LBP)为特征的粒子滤波跟踪算法,降低特征维数的同时建立局部和全局的翼尖特征直方图描述,提高识别效果.同时,该算法在粒子滤波基本框架之下,结...     

噪声干扰环境下基于实测数据的多目标航迹起始算法

雷达在受到噪声干扰的条件下会产生大量杂波.这时,如何完成航迹的快速起始是个值得研究的问题.结合干扰环境下的实测数据,分别采用逻辑法和修正逻辑法对其进行了研究分析,得出了一些有意义的结论.仿真结果表明,该方法很好地克服了3/4逻辑法因虚假目标过多而无法正常起始的问题,并且具有对存储要求较低、计算量较小、便于工程实现的特性...