基于UPF滤波的微小航天器姿态矩阵估计方法

针对基于惯性-星光姿态确定系统噪声存在非高斯分布的情况,提出了将离散粒子滤波(UPF)方法应用于定姿系统滤波器设计,该方法用离散卡尔曼滤波(UKF)得到粒子滤波的重要采样函数,从而克服扩展卡尔曼滤波(EKF)和UKF只能应用到噪声为高斯分布的不足。文章以微机电系统(MEMS)陀螺和互补性金属氧化物半导体有源像素图像传感器(CMOS APS)星敏感器为姿态敏感器件,选取基于矢量观测的最小参数姿态矩阵估计方法为定姿算法,提出将UPF与最小参数姿态矩阵估计方法结合,设计了一种针对微小航天器的UPF姿态估计器,采用从MEMS陀螺采集的数据进行了半物理仿真并对其特性进行了分析与比较。仿真比较结果表明:在敏感器精度较差并且系统噪声非高斯分布的情况下,这种基于UPF的姿态估计方法可以取得比EKF和UKF更快的滤波收敛性和更好的滤波精度,有效地提高了定姿性能。     

一种用于高动态GPS频率估计的滤波算法

针对常用高动态GPS(Global Positioning System)频率估计算法扩展卡尔曼滤波(EKF,Extended Kalman Filter)的缺陷,提出了一种新的称为简化无迹高斯粒子滤波(SUGPF,Simplified Unscented Gaussian Particle Filter)的算法.SUGPF将卡尔曼滤波(KF,Kalman Filter)、无迹卡尔曼滤波(UKF,Unscented Kalman Filter)与高斯粒子滤波(GPF, Gaussian Particle Filter)三者相结合.在时间更新阶段,用KF的方法更新预测分布;在测量更新阶段,用UKF的方法得到重要采样函数,并用GPF的方法更新后验分布.仿真结果表明:与EKF和UKF相比,SUGPF性能更优越,功能更全面,在高斯与非高斯观测噪声环境下均能取得与GPF类似的良好性能,并且其计算复杂度低于GPF.     

基于多重UKF的粗太阳敏感器标定算法研究

针对粗太阳敏感器存在的非余弦特性以及易受空间环境杂光干扰的影响,为了提高粗太阳敏感器的定姿精度,需对其电池片进行标定.通过设计多重UKF(Unscented Kalman Filter)滤波器实现对粗太阳敏感器的定姿标定,将其划分为两类,第一类为标定陀螺漂移,第二类借助陀螺漂移标定结果,进而设计电池片滤波器来精确修正粗太阳敏感器的电池片系数,完成对整个粗太敏电池片的输出数据修正.仿真结果表明,采用多重UKF滤波器算法可有效实现对陀螺和粗太阳敏感器的标定,提高了卫星的姿态确定精度,达到了设计要求.     

基于UPF的修正罗德里格参数卫星姿态估计

粒子滤波(PF)在卫星姿态估计中解决了非高斯分布的情况,在粒子滤波的基础上,利用修正罗德里格参数(MRPs)表示姿态,用动力学方程进行角速率的传播,提出将采样粒子滤波(UPF)算法应用到基于MRPs的卫星姿态估计中,该方法通过UKF得到粒子滤波的重要性采样函数,更充分地利用了最新的量测信息。仿真结果表明,这种基于UPF的姿态估计方法能够获得比UKF和标准PF更高的滤波精度,并且具有更好的滤波稳定性。     

基于Gauss-Newton和UKF结合的微小卫星姿态确定算法

为提高微型低成本姿态敏感器的姿态确定精度,文章基于磁强计/太阳敏感器/陀螺的姿态敏感器配置,设计了高斯牛顿（Gauss-Newton,GN）迭代算法和无迹卡尔曼滤波（Unscented Kalman Filter,UKF）有机结合的微小卫星姿态确定算法,先用Gauss-Newton算法融合磁强计和太阳敏感器的数据,迭代计算最优四元数,然后以最优四元数联合陀螺数据作为观测量,以姿态四元数和惯性系下的角速度为状态量进行UKF,降低观测维数,并将观测方程转化为线性方程,显著减小计算量,同时克服了测量误差对姿态确定精度的影响。     

运用信息融合式高阶UKF的微小卫星姿态确定算法

为提高微小卫星微型低成本姿态敏感器的姿态确定精度,基于磁强计/太阳敏感器/陀螺仪的姿态敏感器配置以及无迹卡尔曼滤波方法（Unscented Kalman Filter,UKF）,设计了一种基于高阶UKF算法并且融合磁强计与太阳敏感器观测信息的微小卫星姿态确定算法.为提高系统状态方程非线性函数的一步预测精度,采用基于五阶UT变换的高阶UKF算法,增加了Sigma采样点数量,提高了系统状态预测精度.单一观测向量滤波算法不能同时满足多个不同量纲观测数据,本文提出一种同时利用两个观测向量的信息融合式滤波算法,根据磁强计和太阳敏感器的观测信息,通过卡尔曼滤波原理中的增益计算,分别得出地磁矢量和太阳矢量对应的卡尔曼增益信息.采用高斯概率密度准则进行信息融合,进而完成预测值的修正,得到同时满足磁强计以及太阳敏感器观测需求的四元数估计值,降低了观测误差的影响.仿真分析验证了算法的优越性.      

航天器天文导航中星敏感器最佳安装方位研究

航天器自主天文导航中,通常用星敏感器和地平仪测量的星光角距作为观测量,星敏感器安装方位角是影响导航精度的一个重要因素.针对星光角距作为观测量的自主天文导航方法,分别采用扩展卡尔曼滤波EKF(Extended Kalman Filter)和Unscented卡尔曼滤波UKF(Unscented Kalman Filter)2种滤波方法进行仿真计算,研究了星敏感器安装方位角对导航精度的影响规律,得出星敏感器的最佳安装方位角,并考察了其在不同轨道参数下的适用性,为星敏感器的安装和星敏感器视场内观测星的选取提供了依据.仿真计算表明,本结论对其它利用"星光+地平"的自主导航方法也适用.      

基于UKF的雷达高度计自主定轨

探讨了利用推广卡尔曼滤波估计非线性系统状态时存在的问题,从而介绍了目前广泛使用的分步逼近的卡尔曼滤波(UKF,Unscented Kalman Filter).为了提高导航的可靠性和准确性,在星敏感器导航系统中引入雷达高度计作为一个新的测量设备,提出了一种基于星上雷达测高仪及星敏感器联合进行卫星自主定轨的算法.建立了比较复杂的地球海平面模型,并考虑了其中风生重力波的影响. 利用雷达测高仪的测量结果和地球形状模型,计算地心矢量在卫星本体中坐标系的方向.利用UKF滤波定轨算法,明显提高了自主定轨的精度.数值仿真结果表明,UKF定轨精度要远优于推广卡尔曼滤波.      

基于矢量观测的航天器分段信息融合定姿方法

针对基于MEMS(微机电系统)陀螺和CMOS APS星敏感器的集成惯性/星光姿态确定系统的低精度特点,研究了适用于该定姿系统的基于矢量观测的定姿算法.对于陀螺/星敏感器这种配置模式,有EKF(Extended Kalman Filter)、QUEST、最优REQUEST等几种适用的定姿算法.针对EKF和最优REQUEST算法的不同特点并结合确定性算法QUEST,以四元数为姿态参数,将姿态估计的EKF方法分别与QUEST算法和最优REQUEST算法进行了融合,提出一种分段信息融合的姿态估计器:陀螺漂移估计误差较大时,将EKF与QUEST结合,快速估计出陀螺漂移.当陀螺漂移误差减小到一定程度,再切换为EKF与最优REQUEST算法融合的双重滤波器.仿真比较结果表明,这种分段信息融合的姿态估计器既可以估计姿态参数也可以估计陀螺漂移,并能达到很高的定姿精度.     

多星敏感器测量最优姿态估计算法

多数利用星敏感器加陀螺组合的姿态确定方法中,由于星敏感器精度较高,使得系统定姿的精度比较高.然而,姿态确定的算法因观测模型和误差处理不当,导致滤波器观测修正能力下降,从而不能有效地估计陀螺的漂移误差.提出了基于星敏感器观测姿态角的误差建模,研究了多星敏感器组合的最优安装构型和观测融合方法.利用加权最小二乘法对观测数据的预处理,使观测方程定常化.再利用陀螺加星敏感器组合的扩展Kalman滤波(EKF,Extended Kalman Filtering)对航天器姿态和陀螺漂移进行估计.仿真结果表明,提出的多星敏感器最优组合的滤波方法能够有效精确地估计卫星三轴姿态和陀螺漂移,且该方法计算量小,有利于卫星定姿系统的在轨自主运行.     

The attitude inversion method of geostationary satellites based on unscented particle filter

The attitude information of geostationary satellites is difficult to be obtained since they are presented in non-resolved images on the ground observation equipment in space object surveillance. In this paper, an attitude inversion method for geostationary satellite based on Unscented Particle Filter (UPF) and ground photometric data is presented. The inversion algorithm based on UPF is proposed aiming at the strong non-linear feature in the photometric data inversion for satellite attitude, which combines the advantage of Unscented Kalman Filter (UKF) and Particle Filter (PF). This update method improves the particle selection based on the idea of UKF to redesign the importance density function. Moreover, it uses the RMS-UKF to partially correct the prediction covariance matrix, which improves the applicability of the attitude inversion method in view of UKF and the particle degradation and dilution of the attitude inversion method based on PF. This paper describes the main principles and steps of algorithm in detail, correctness, accuracy, stability and applicability of the method are verified by simulation experiment and scaling experiment in the end. The results show that the proposed method can effectively solve the problem of particle degradation and depletion in the attitude inversion method on account of PF, and the problem that UKF is not suitable for the strong non-linear attitude inversion. However, the inversion accuracy is obviously superior to UKF and PF, in addition, in the case of the inversion with large attitude error that can inverse the attitude with small particles and high precision.     

Central difference predictive filter for attitude determination with low precision sensors and model errors

Attitude determination is one of the key technologies for Attitude Determination and Control System (ADCS) of a satellite. However, serious model errors may exist which will affect the estimation accuracy of ACDS, especially for a small satellite with low precision sensors. In this paper, a central difference predictive filter (CDPF) is proposed for attitude determination of small satellites with model errors and low precision sensors. The new filter is proposed by introducing the Stirling’s polynomial interpolation formula to extend the traditional predictive filter (PF). It is shown that the proposed filter has higher accuracy for the estimation of system states than the traditional PF. It is known that the unscented Kalman filter (UKF) has also been used in the ADCS of small satellites with low precision sensors. In order to evaluate the performance of the proposed filter, the UKF is also employed to compare it with the CDPF. Numerical simulations show that the proposed CDPF is more effective and robust in dealing with model errors and low precision sensors compared with the UKF or traditional PF.     

The adaptive Gaussian mixtures unscented Kalman filter for attitude determination using light curves

The Adaptive Gaussian Mixtures Unscented Kalman Filter (AGMUKF) is introduced to estimate the attitude of a Resident Space Object using light curves. This filter models the state probability density function as a Gaussian Mixture. This enables to capture the non-linearities of the light-curve measurement model. A non-linearity index is used to refine the mixture when necessary, and individual Gaussian kernels are merged back together when their relative distance is below a certain threshold. A conventional attitude Unscented Kalman Filter (UKF) is used to propagate and update each kernel. The AGMUKF efficiently maintains the mixture population as low as possible, while still being able to represent non-symmetric, multimodal, arbitrarily complex distributions. Therefore, it is presented as a promising alternative to Particle-Filter-based implementations, the current state of the art used in sequential attitude estimation from light curves. The non-linearity index has been used to show that the measurement model is the main contributor to the system non-linearity. Results have demonstrated the superiority of the AGMUKF w.r.t. the UKF for attitude determination, and that it can converge for high initial state uncertainty cases, successfully capturing the non-Gaussian probability distribution of the state.     

一种基于自适应滤波的GPS滚转角估计方法

全球定位系统（GPS）测姿技术主要是利用GPS载波相位和信号功率2种方法,但是测量信息单一且独立,针对旋转载体的测姿问题,提出一种基于自适应滤波的GPS滚转角估计方法,通过融合GPS接收机天线信号功率和多普勒频率信息测量载体滚转角和滚转角速度。利用当前统计模型对滚转角和滚转角速度测量进行系统建模,根据滚转角预测估计值选取量测量,并提出自适应滤波,采用滚转角加速度估计自适应滤波算法,实现了对系统噪声方差阵的自适应调整,避免了滚转角加速度最值的选取问题,降低了噪声的影响。通过仿真验证了基于自适应滤波的GPS滚转角估计方法的可行性,结果表明该方法的测量精度高于无迹卡尔曼滤波（UKF）。      

比例UT变换的一种比例因子自适应选取方法

将比例UT(Unscented Transform)变换应用于一般的采样策略可以解决采样的非局部效应问题,提高UKF(Unscented Kalman Filtering)的估计精度,而比例因子是其中的重要参数。文章提出了一种比例因子的自适应选取方法,将自适应的比例UT变换作用于最小偏度单形采样,并把相应的UKF应用于低轨卫星实时定轨中。仿真结果表明,这种自适应选取参数方法稳定可靠,其相应的UKF定轨精度与最佳的固定比例因子UKF的精度相当。     

深空探测器接近段自适应滤波方法研究

天文导航是一种广泛应用于深空探测任务的全自主导航方法.基于状态模型和量测模型的非线性卡尔曼滤波方法在天文导航中被广泛使用.卡尔曼滤波要求状态和量测模型误差是高斯白噪声且先验协方差信息已知,但在深空探测器天文导航系统中,状态模型和量测模型噪声通常不能精确知道且是时变的.因此,自适应卡尔曼滤波器广泛用于解决状态和量测模型误差未知且时变的问题.本文首先结合火星探测器接近段的实际情况分析了火星探测器接近段模型噪声的时变特性,然后对三种常用的在线调节自适应滤波方法在火星探测接近段的滤波表现进行了仿真实验.