伪码测距中处理速率对再生时钟抖动性能的影响

在微小卫星再生伪码测距中,码同步环的跟踪性能是决定测距精度的关键,而数字处     

天基照相跟踪空间碎片批处理轨道确定研究

随着国内外天基观测空间碎片研究的展开,文章提出了利用跟踪卫星的CCD(Charge
Coupled Device)相机对空间碎片进行轨道探测的方法,首先建立了CCD照相观测模型和基于 照相观测 的空间碎片批处理轨道确定模型。通过对CCD相机底片归算方法的分析可知,利用
CCD相机所获得的观测数据与跟踪卫星的姿态无关,且其精度只与测量和坐标转换计算的精 度有关,在测量和计算中可获得较高的精度。分别对分布密度较高的低轨道和地球同步 轨道区域的空间碎片进行了定轨分析。仿真结果表明,定轨时采用两个跟踪弧段的照相数据 定轨精度大大高于一个弧段照相数据的定轨精度;跟踪卫星距离空间碎片越近,定轨精度越 高;低轨道空间碎片的定轨精度高于地球同步轨道上的空间碎片定轨精度。
     

基于归一化神经网络的航天器自适应姿态跟踪控制

针对以变速控制力矩陀螺（VSCMGs）为姿态控制执行机构的航天器在同时考虑惯性参数和执行机构不确定性情况下的姿态跟踪控制问题,提出了一种基于归一化神经网络的自适应姿态跟踪控制方法。设计一个非线性反馈控制器作为航天器姿态控制的基本控制器,利用归一化神经网络设计补偿控制器,用以在线估计和消除包含系统不确定参数的未知不确定函数的影响,避免了标准自适应控制方法需要进行大量不确定参数估计的缺陷。采用神经网络输入归一化技术,简化了闭环系统复杂的稳定性分析过程。理论分析证明了闭环系统的稳定性和姿态跟踪误差的收敛性。仿真结果表明,所提出的控制方法能满足航天器在惯性参数和执行机构不确定性及外干扰存在情况下的高精度姿态跟踪控制要求。
     

一种步长为对角阵的正交数据投影算法

针对传统子空间跟踪算法正交性和稳定性差的问题,基于数据投影算法(DPM),提出了一种步长为对角阵的正交DPM算法。算法运算复杂度较低,能提供标准正交的子空间,运行时无累积性误差;采用"自适应"而非固定搜索步长,能更好地匹配子空间的动态收敛速度,可进一步提高收敛速率并具有更佳的跟踪性能。仿真结果证明了新算法的有效性和正确性。     

基于Simulink的直接序列扩频通信系统的仿真

卫星通信系统测试扩频信号源需具备自检功能。文章利用MATLAB的可视化工具箱Simulink建立了直扩通信系统的仿真模型,为扩频信号源自检程序设计提供参考。采用滑动相关捕获算法、单△值非相干延迟锁定伪码跟踪环,实现了伪码同步。仿真结果表明,该系统可无误码地恢复发端原始信息。     

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。      

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.