Real-time sensor fault detection and isolation for LEO satellite attitude estimation through magnetometer data

Attitude estimation is a critical component of the Attitude Determination and Control System (ADCS) of any satellite. It is used to convert the sensor observation data to an estimated attitude using filtering algorithms. However, in the presence of sensor faults, the ADCS fails to achieve the desired attitude accuracy. In this paper, the Fault Tolerant Extended Kalman Filter (FTEKF) is proposed to handle this imperfection. In accordance, various filtering steps are included in the FTEKF design to enhance both attitude estimation and sensor fault detection. The developed algorithm can detect and isolate any unexpected sensor faults in real time, which provides a reliable attitude estimation. A comparative study with the classical and robust Kalman filters is performed through numerical simulations in order to validate the effectiveness of the adopted filter in case of magnetometer fault data.     

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.     

Doppler utilised Kalman estimation (DUKE) of ionospheric delay for satellite navigation

The ionospheric delay experienced by the satellite navigation signals depends upon the Total Electron Content (TEC) and needs to be corrected. While the single frequency receivers always use parametric models to correct this delay, dual frequency receivers, when suffers a loss of lock of one of its signal, also has to resort to these models. Here, an alternative method, based on Doppler, surrogated by range rate variation, has been attempted to estimate the ionospheric delay using a Kalman filter. GPS data have been used for all visible satellites over four days selected around the equinox and solstice with nominal geomagnetic conditions and estimations done in continuous and calibrated modes. Results of continuous estimation, obtained for a mid latitude station, showed moderate accuracy while it was significantly better for the calibrated mode with no seasonal dependence. Estimations done for station within the extent of equatorial anomaly, has not only resulted in relative deterioration in performance, but also shown seasonal dependence. Compared with estimates of Klobuchar model, the Calibrated estimation showed superior performance, conspicuously in the mid latitude station. However, for the continuous mode, performance was at par with the model at higher latitudes but inferior to it in regions within the extent of the equatorial anomaly.     

GPS/DRS/DMAP汽车定位导航系统

阐述了一种基于单片机的由速率陀螺、磁罗盘和里程仪组成的新型航迹推算系统及其工作原理,在此基础上设计了多级滤波组合方式GPS(Global Positioning System)/DRS(Dead Reckoning System)汽车定位导航系统,并通过地图匹配进一步提高定位精度,同时利用GSM(Global System for Mobile communications)进行定位数据的无线传输.跑车实验表明该系统具有较高的定位精度和可靠性.     

基于GPR模型的自适应平方根容积卡尔曼滤波算法

与传统算法一样,动态系统的参数化模型(含噪声统计特性)未知或不够准确易导致容积卡尔曼滤波(CKF)效果严重下降,甚至滤波结果发散.为此,利用高斯过程回归(GPR)方法对训练数据进行学习,得到动态系统的状态转移GPR模型和量测GPR模型以及噪声统计特性,用以替代或增强原有动态系统模型,并将其融入到平方根容积卡尔曼滤波(SRCKF)中,分别提出了无模型高斯过程SRCKF (MFGP-SRCKF)和模型增强高斯过程SRCKF (MEGP-SRCKF)两种算法.仿真结果表明:这两种新的自适应滤波算法提高了动态系统模型精度,且实时自适应调整噪声的协方差,克服了传统算法滤波性能易受系统模型限制的问题;与MFGP-SRCKF相比,在给定一个不够准确的参数化模型,且有限的训练数据未能遍布估计状态空间的情况下,MEGP-SRCKF具备更高的滤波精度.     

Model based Computerized Ionospheric Tomography in space and time

Reconstruction of the ionospheric electron density distribution in space and time not only provide basis for better understanding the physical nature of the ionosphere, but also provide improvements in various applications including HF communication. Recently developed IONOLAB-CIT technique provides physically admissible 3D model of the ionosphere by using both Slant Total Electron Content (STEC) measurements obtained from a GPS satellite - receiver network and IRI-Plas model. IONOLAB-CIT technique optimizes IRI-Plas model parameters in the region of interest such that the synthetic STEC computations obtained from the IRI-Plas model are in accordance with the actual STEC measurements. In this work, the IONOLAB-CIT technique is extended to provide reconstructions both in space and time. This extension exploits the temporal continuity of the ionosphere to provide more reliable reconstructions with a reduced computational load. The proposed 4D-IONOLAB-CIT technique is validated on real measurement data obtained from TNPGN-Active GPS receiver network in Turkey.     

MCMC-Particle-based group tracking of space objects within Bayesian framework

With the intense increase in space objects, especially space debris, it is necessary to efficiently track and catalog the extensive dense clusters of space objects. As the main instrument for low earth orbit (LEO) space surveillance, ground-based radar system is usually limited by its resolution while tracking small space debris with high density. Thus, the obtained measurement information could have been seriously missed, which makes the traditional tracking method inefficient. To address this issue, we conceived the concept of group tracking. For group tracking, the overall tendency of the group objects is expected to be revealed, and the trajectories of individual objects are simultaneously reconstructed explicitly. According to model the interaction between the group center and individual trajectories using the Markov random field (MRF) within Bayesian framework, the objects’ number and individual trajectory can be estimated more accurately in the condition of high miss alarm probability. The Markov chain Monte Carlo (MCMC)-Particle algorithm was utilized for solving the Bayesian integral problem. Furthermore, we introduced the mechanism for describing the behaviors of groups merging and splitting, which can expand the single group tracking algorithm to track variable multiple groups. Finally, simulation of the group tracking of space objects was carried out to validate the efficiency of the proposed method.     

深空探测航天器巡航段自主导航方法研究

对深空探测航天器自主导航方法进行了研究。为了应对深空探测中航天器轨道动力学模型的误差，在分光计测量航天器相对于太阳径向速度基础上，引入了小行星的视线矢量测量。通过最小二乘法计算出由小行星视线矢量所得到的位置信息，采用改进的信息融合方法修正扩展卡尔曼滤波中不精确的动力学模型造成的状态估计误差。同时计算了模型的能观度，对模型的可观性进行了分析。最后对算法进行了仿真分析，仿真结果表明，该算法对动力学模型的依赖性明显低于其他算法，在相同模型精度下，可获得更好的滤波精度。     

Optimal robust non-fragile Kalman-type recursive filtering with finite-step autocorrelated noises and multiple packet dropouts

In this paper, the optimal robust non-fragile Kalman-type recursive filtering problem is studied for a class of uncertain systems with finite-step autocorrelated measurement noises and multiple packet dropouts. The system state, measurement output and filter parameters are all subject to stochastic uncertainties or multiplicative noises, where the measurement noises are finite-step autocorrelated. When there exist multiple packet dropouts in the system output, the original system is converted into an auxiliary stochastic uncertain system by the augmentation of system states and measurements. The process noises and measurement noises of the auxiliary system are shown to be finite-step autocorrelated and cross-correlated. Then, a robust non-fragile Kalman-type recursive filter is designed that is optimal in the minimum-variance sense. The proposed filter is not only robust against the uncertainties in the system model and measurement model, but also non-fragile against the implementation error with the filter parameters. Simulation results are employed to demonstrate the effectiveness of the proposed method.     

卡尔曼滤波在自旋卫星章动控制中的应用

细长体结构卫星的自旋运动是不稳定的,章动有发散趋势,须进行实时的章动监视,且章动角的测量值包含有误差.通过引入一个离散控制过程的系统,介绍了卡尔曼滤波器的基本原理,建立了自旋卫星自旋运动简化的动力学模型,给出了运动方程和观测方程,详细地推导了适用于自旋卫星章动控制的卡尔曼滤波处理过程,利用该模型对某自旋卫星章动进行仿真计算,并对姿态章动联合控制和应急章动控制情况下的章动控制应用情况进行分析,得出结论：基于卡尔曼滤波的卫星章动角计算的方法正确、结果准确.