一种改进的星敏感器/陀螺卫星定姿算法

针对传统的四元数估计(quaternion estimation,QUEST)算法在观测矢量之间夹角很小或噪声剧烈增大时,定姿性能显著降低的问题,提出一种基于当前星敏感器观测信息量的滤波算法。该算法通过引入动态增益系数,解决了历史观测信息和当前观测信息之间的比例问题。该系数能够根据当前观测矢量信息量的大小变化自适应调整,从而避免了需要另外一个滤波过程对其进行确定的复杂性。对该算法进行了仿真,对比结果表明,该算法可以有效提高系统的定姿性能。     

REKF and RUKF for pico satellite attitude estimation in the presence of measurement faults

When a pico satellite is under normal operational condi- tions, whether it is extended or unscented, a conventional Kalman filter gives sufficiently good estimation results. However, if the measurements are not reliable because of any kind of malfunc- tions in the estimation system, the Kalman filter gives inaccurate results and diverges by time. This study compares two different robust Kalman filtering algorithms, robust extended Kalman filter （REKF） and robust unscented Kalman filter （RUKF）, for the case of measurement malfunctions. In both filters, by the use of de- fined variables named as the measurement noise scale factor, the faulty measurements are taken into the consideration with a small weight, and the estimations are corrected without affecting the characteristic of the accurate ones. The proposed robust Kalman filters are applied for the attitude estimation process of a pico satel- lite, and the results are compared.     

Modified unscented particle filter for nonlinear Bayesian tracking

A modified unscented particle filtering scheme for nonlinear tracking is proposed, in view of the potential drawbacks （such as, particle impoverishment and numerical sensitivity in calculating the prior） of the conventional unscented particle filter （UPF） confronted in practice. Specifically, a different derivation of the importance weight is presented in detail. The proposed method can avoid the calculation of the prior and reduce the effects of the impoverishment problem caused by sampling from the proposal distribution, Simulations have been performed using two illustrative examples and results have been provided to demonstrate the validity of the modified UPF as well as its improved performance over the conventional one.     

基于MEKF的卫星姿态确定精度影响因素分析

乘性扩展卡尔曼滤波(multiplicative extended Kalman filter, MEKF)方法是卫星姿态确定中最为成熟有效的方法之一。针对该姿态确定方法,采用EKF随机稳定性分析理论,定性地分析了MEKF姿态确定精度的影响因素及其影响情况,重点分析了星敏感器采样频率、卫星姿态角速度的大小、初始误差、滤波参数选取等因素对MEKF姿态确定精度的影响,并通过仿真实验验证了上述分析的合理性。上述研究不仅能够加深人们在理论上对MEKF姿态确定方法的认识,也能够为工程上利用MEKF进行姿态确定提供参考。     

Simplified unscented particle filter for nonlinear/non-Gaussian Bayesian estimation

Particle filters have been widely used in nonlinear/nonGaussian Bayesian state estimation problems.However,efficient distribution of the limited number of particles in state space remains a critical issue in designing a particle filter.A simplified unscented particle filter(SUPF) is presented,where particles are drawn partly from the transition prior density(TPD) and partly from the Gaussian approximate posterior density(GAPD) obtained by a unscented Kalman filter.The ratio of the number of particles drawn from TPD to the number of particles drawn from GAPD is adaptively determined by the maximum likelihood ratio(MLR).The MLR is defined to measure how well the particles,drawn from the TPD,match the likelihood model.It is shown that the particle set generated by this sampling strategy is more close to the significant region in state space and tends to yield more accurate results.Simulation results demonstrate that the versatility and estimation accuracy of SUPF exceed that of standard particle filter,extended Kalman particle filter and unscented particle filter.     

车载DR导航的非线性滤波方法研究

针对扩展卡尔曼滤波方法(EKF)用于车载DR导航系统滤波中存在的一些缺点,将一种新的滤波方法—UKF滤波方法用于车载DR导航系统的非线性状态估计中。该滤波方法与EKF方法相比具有容易实现和滤波精度高的特点。通过非线性状态估计UKF方法大大提高了导航系统的精度。为了检验其有效性,将这两种方法分别对车载DR导航系统进行滤波仿真,仿真结果进一步表明UKF方法优于EKF方法,是一种理想的车载DR导航非线性滤波方法。     

UKF-based multi-sensor passive tracking with active assistance

1 .INTRODUCTIONRadar can provide complete position informationand/or Doppler information of target ,but it emitselectromagnetic wave when it works , so it easilysuffers fromelectromagnetic interference and anti-radiation missile’s attack. On the contrary ,infra-red doesn’t emit any energy ,it detects andlocatestarget by receiving thermal energy radiated fromtarget , so it has the powerful anti-interferenceability .Just because infrared doesn’t radiate anyenergy ,it can’t be easily det…     

伪陀螺/星敏感器组合双体卫星姿控系统

针对双体卫星姿态控制系统,给出了"伪陀螺"概念,及其与星敏感器组合的姿态确定方法,其中伪陀螺是使用卫星内部传感器的测量以及卫星参数来进行动态模型的实时软计算,跟踪系统角动量进而得到角速度。首先建立了双体卫星模型,并设计了动量轮控制律,在此基础上根据星敏感器的测量信息,应用广义卡尔曼滤波器来估计姿态和角速率漂移误差,进而对伪陀螺和惯性陀螺进行校正。仿真结果表明本文的姿态确定方法,对双体卫星的姿态控制系统是非常有效的。     

基于UKF的单站无源定位与跟踪双向预测滤波算法

提出了一种新的滤波算法,以加快滤波算法的收敛速度和提高滤波的估计精度。反向预测与更新提高了上一时刻状态估计的精度,减小了当前时刻的状态预测误差。利用更准确的初始条件经过正向预测与更新,能得到当前状态更精确的估计值。计算机仿真结果表明,本算法的滤波性能优于传统的迭代滤波算法,既提高了滤波的估计精度,又加快了算法的收敛速度。     

误差方差约束下不确定离散时间系统的鲁棒卡尔曼滤波

本文研究状态矩阵及测量矩阵中均含有不确定性的离散时间系统的鲁棒卡尔曼滤波问题。在状态估计领域中，指标要求常直接以状态分量的估计误差方差上限的形式给出。为此，本文的目的在于设计卡尔曼滤波增益，使不确定系统的估计误差方差达到稳态且其值不大于预先指定值、文中给出了期望鲁棒滤波增益的存在条件及其解析表达式，并以数值算例说明设计方法的直接性与有效性。     

光电跟踪系统中两步非线性滤波算法研究

基于非线性最小二乘准则的两步最优估计方法(Two-step optimal estimation, TSE)在第二步中需要计算雅克比矩阵的逆,且其逆的计算经常是不存在的,从而导致滤波结果发散.因此,为改进TSE算法的稳定性,在分析了TSE算法的原理的基础上,提出了改进的TSE算法,并确定了TSE算法的中间状态向量和转换矩阵的选取原则.通过非线性测量光电跟踪系统的仿真实验验证了所提出的改进的TSE算法可以保证算法的稳定性、中间状态向量和转换矩阵的选取原则的正确性,同时也证明了此算法的性能优于扩展卡尔曼滤波和U卡尔曼滤波.     

Fault tolerant navigation method for satellite based on information fusion and unscented Kalman filter

An effective autonomous navigation system for the integration of star sensor, infrared horizon sensor, magnetometer, radar altimeter and ultraviolet sensor is developed. The requirements of the integrated navigation system manager make optimum use of the various navigation sensors and allow rapid fault detection, isolation and recovery. The normal full fusion feedback method of federated unscented Kalman filter (UKF) cannot meet the needs of it. So a no-reset feedback federated Kalman filter architecture is developed and used in the autonomous navigation system. The minimal skew sigma points are chosen to improve the calculation speed. Simulation results are presented to demonstrate the advantages of the algorithm. These advantages include improved failure detection and correction, improved computational efficiency, and reliability. Additionally, its’ accuracy is higher than that of the full fusion feedback method.     

一种基于奇异值分解的非线性滤波新算法

提出一种基于奇异值分解的无导数卡尔曼非线性滤波新算法对UKF算法进行改进.该算法利用奇异值分解作为工具,将原算法中的协方差矩阵进行奇异值分解,可以在一定程度上避免在递推过程中,由于计算误差和舍入误差而引起的协方差矩阵失去正定性,从而导致算法失效的问题.在不降低滤波精度,不增加算法复杂度的前提下,新算法具有很好的数值稳定性.实例仿真结果验证了本方法的有效性.     

基于UKF的导弹SINS/CNS姿态估计方法

针对中远程弹道导弹的特点,在分析研究捷联惯性导航系统/天文导航系统(strapdown inertial navigation system/celestial navigation system, SINS/CNS)组合导航测量修正方案的基础上,建立了导弹四元数运动学方程、陀螺测量模型,星敏感器测量模型等系统方程,将无轨迹卡尔曼滤波(unscented Kalman filter, UKF)算法应用于主动段关机点环境,通过对陀螺常值漂移、一阶漂移的在线估计,达到高精度导弹姿态的实时输出。与EKF、QUEST、MLS和捷联惯性迭代递推算法比较,仿真结果表明了UKF算法具有更高的精度,收敛快,适于在无控飞行阶段的工程应用。     

Design and simulation of fault diagnosis based on NUIO/LMI for satellite attitude control systems

This paper presents a scheme of fault diagnosis for flexible satellites during orbit maneuver. The main contribution of the paper is related to the design of the nonlinear input observer which can avoid false alarm arising from the disturbance from orbit control force. The effects of orbit control force on the fault diagnosis system for satellite attitude control systems, including the disturbing torque caused by the misalignments and the model uncertainty caused by the fuel consumed, are discussed, where standard Luenberger observer cannot work well. Then the nonlinear unknown input observer is proposed to decouple faults from disturbance. Besides, a linear matrix inequality approach is adopted to reduce the effect of nonlinear part and model uncertainties on the observer. The numerical and semi-physical simulation demonstrates the effectiveness of the proposed observer for the fault diagnosis system of the satellite during orbit maneuver.     

迭代无味卡尔曼滤波器的算法实现与应用评价

为了对各种迭代无味卡尔曼滤波(iterated unscented Kalman filter, IUKF)算法的应用及性能表现给出较为全面、客观的评价,分别导出并探讨了3种IUKF算法之间的内在联系。多种情况下的仿真应用表明,当观测噪声不太大,且该非线性系统状态的后验密度为可用高斯分布很好近似的单峰形式时,或者说是引起系统非线性的状态量是完全瞬时可观测时,选用恰当的IUKF算法,通过2~3次迭代,就可以在保持滤波一致性的条件下,进一步获得显著的精度收益;否则,IUKF相对于无味卡尔曼滤波(unscented Kalman filter, UKF)的迭代收益就难以保证。     

基于自适应UKF算法的机载INS/GPS空中对准研究

在空中对准失准角不满足小角度假设的条件下,推导了一种新的机载INS/GPS大失准角空中对准的误差模型。将基于极大似然估计的自适应估计器与无迹卡尔曼滤波(unscented Kalman filter, UKF)算法相结合,修改自适应滤波算法中自适应参数的表达式。提出将自适应UKF算法用于非线性误差模型的空中对准方案中。仿真表明,自适应UKF算法能够克服噪声统计模型不准确对滤波结果的影响,失准角估计的精度好于UKF算法的精度。     

大型柔性卫星平台的H∞回路成形姿态稳定控制

研究了大型柔性卫星平台H∞回路成形姿态稳定控制问题.首先利用拉格朗日方程建立了该卫星的动力学方程,描述了系统存在的非结构不确定性,然后设计了H∞回路成形控制器.提出了控制系统所需要满足的鲁棒稳定性和鲁棒性能指标,随后利用H∞回路成形理论设计了卫星的姿态稳定控制器,最后通过数学仿真证明了所设计的控制器的有效性.     

基于改进因子图模型的航天器组合姿态确定算法

航天器姿态确定是航天器姿轨控制、在轨正常运行的关键,针对航天器多姿态传感器存在测量噪声非高斯分布、可能出现敏感器失效和故障等问题,提出了一种基于改进因子图模型的航天器组合姿态确定方法.通过建立因子图模型,将地磁/星敏/陀螺测量信息作为因子节点加入因子图模型,利用观测蒸馏法对观测数据集进行提炼以及自适应调整,实现对航天器...     

基于PSO的USQUE在组合导航姿态估计中的应用

针对组合导航姿态估计中无味四元数估计(unscented quaternion estimation, USQUE)的噪声协方差矩阵参数无法准确给出等问题,提出基于粒子群优化的USQUE(USQUE based on particle swarm optimization, PSO-USQUE)算法。通过粒子群算法对噪声协方差矩阵Q和R进行寻优,获取优化的噪声协方差矩阵等滤波先验条件;分别进行仿真实验和微机电惯导系统/GPS车载实验。实验结果表明,对于USQUE的姿态估计问题, PSO-USQUE算法相比常规算法具有更高的精度,验证了所提算法的有效性。