Intelligent fault-tolerant algorithm with two-stage and feedback for integrated navigation federated filtering

In order to take full advantage of federated filter in fault-tolerant design of integrated navigation system, the limitation of fault detection algorithm for gradual changing fault detection and the poor fault tolerance of global optimal fusion algorithm are the key problems to deal with. Based on theoretical analysis of the influencing factors of federated filtering fault tolerance, global fault-tolerant fusion algorithm and information sharing algorithm are proposed based on fuzzy assessment. It achieves intelligent fault-tolerant structure with two-stage and feedback, including real-time fault detection in sub-filters, and fault-tolerant fusion and information sharing in main filter. The simulation results demonstrate that the algorithm can effectively improve fault-tolerant ability and ensure relatively high positioning precision of integrated navigation system when a subsystem having gradual changing fault.

Multi-sensor federated unscented Kalman filtering algorithm in intermittent observations

Aiming at the adverse effect caused by limited detecting probability of sensors on filtering precision of a nonlinear system state,a novel multi-sensor federated unscented Kalman filtering algorithm is proposed.Firstly,combined with the residual detection strategy,effective observations are correctly identified.Secondly,according to the missing characteristic of observations and the structural feature of unscented Kalman filter,the iterative process of the single-sensor unscented Kalman filter in intermittent observations is given.The key idea is that the state estimation and its error covariance matrix are replaced by the state one-step prediction and its error covariance matrix,when the phenomenon of observations missing occurs.Finally,based on the realization mechanism of federated filter,a new fusion framework of state estimation from each local node is designed.And the filtering precision of system state is improved further by the effective management of observations missing and the rational utilization of redundancy and complementary information among multi-sensor observations.The theory analysis and simulation results show the feasibility and effectiveness of the proposed algorithm.

Pulsar/CNS integrated navigation based on federated UKF

In order to improve the autonomous navigation capability of satellite,a pulsar/CNS（celestial navigation system） integrated navigation method based on federated unscented Kalman filter（UKF） is proposed.The celestial navigation is a mature and stable navigation method.However,its position determination performance is not satisfied due to the low accuracy of horizon sensor.Single pulsar navigation is a new navigation method,which can provide highly accurate range measurements.The major drawback of single pulsar navigation is that the system is completely unobservable.As two methods are complementary to each other,the federated UKF is used here for fusing the navigation data from single pulsar navigation and CNS.Compared to the traditional celestial navigation method and single pulsar navigation,the integrated navigation method can provide better navigation performance.The simulation results demonstrate the feasibility and effectiveness of the navigation method.

Adaptive Federal Kalman Filtering for SINS/GPS Integrated System

A new adaptive federal Kalman filter for a strapdown integrated navigation system/global positioning system (SINS/GPS) is given. The developed federal Kalman filter is based on the trace operation of parameters estimation's error covariance matrix and the spectral radius of update measurement noise variance-covariance matrix for the proper choice of the filter weight and hence the filter gain factors. Theoretical analysis and results from simulation in which the SINS/GPS was compared to conventional Kalman filter are presented. Results show that the algorithm of this adaptive federal Kalman filter is simpler than that of the conventional one. Furthermore, it outperforms the conventional Kalman filter when the system is undertaken measurement malfunctions because of its possession of adaptive ability. This filter can be used in the vehicle integrated navigation system.

Federal extended Kalman filter based on reconstructed observation in incomplete observations

In the estimation and identification of nonlinear system state,aiming at the adverse effect of observation missing randomly caused by detection probability of used sensor which is less than 1,a novel federal extended Kalman filter( FEKF) based on reconstructed observation in incomplete observations( ROIO) is proposed in this paper. On the basis of multi-sensor observation sets,the observation is exchanged at different times to construct a new observation set. Based on each observation set,an extended Kalman filter algorithm is used to estimate the state of the target,and then the federal filtering algorithm is used to solve the state estimation based on the multi-sensor observation data. The effect of the sensor probing probability on the filtering result and the effect of the number of sensors on the filtering result are obtained by the simulation experiment,respectively. The simulation results demonstrate effectiveness of the proposed algorithm.

Implementation and Validation of the Optimized Deduplication Strategy in Federated Cloud Environment

Cloud computing technology is the culmination of technical advancements in computer networks,hardware and software capabilities that collectively gave rise to computing as a utility.It offers a plethora of utilities to its clients worldwide in a very cost-effective way and this feature is enticing users/companies to migrate their infrastructure to cloud platform.Swayed by its gigantic capacity and easy access clients are uploading replicated data on cloud resulting in an unnecessary crunch of storage in datacenters.Many data compression techniques came to rescue but none could serve the purpose for the capacity as large as a cloud,hence,researches were made to de-duplicate the data and harvest the space from exiting storage capacity which was going in vain due to duplicacy of data.For providing better cloud services through scalable provisioning of resources,interoperability has brought many Cloud Service Providers(CSPs)under one umbrella and termed it as Cloud Federation.Many policies have been devised for private and public cloud deployment models for searching/eradicating replicated copies using hashing techniques.Whereas the exploration for duplicate copies is not restricted to any one type of CSP but to a set of public or private CSPs contributing to the federation.It was found that even in advanced deduplication techniques for federated clouds,due to the different nature of CSPs,a single file is stored at private as well as public group in the same cloud federation which can be handled if an optimized deduplication strategy be rendered for addressing this issue.Therefore,this study has been aimed to further optimize a deduplication strategy for federated cloud environment and suggested a central management agent for the federation.It was perceived that work relevant to this is not existing,hence,in this paper,the concept of federation agent has been implemented and deduplication technique following file level has been used for the accomplishment of this approach.

多IRST自适应协同定位技术研究

为解决传统单红外搜索与跟踪(IRST)系统观测度不足,无法提供目标全维态势信息的问题,提出了基于几何精度因子(GDOP)的多IRST自适应协同定位技术。其核心思想为对多IRST进行双机组合配对,依据配对组合的GDOP值构造面向定位精度的信息类函数,进而确定联邦滤波信息分配系数,最后基于自适应联邦滤波算法解决双IRST配对组合局部估计的全局信息融合问题,得到目标的精确位置信息。通过仿真结果证明,所提算法定位精度高、可靠性强,同时,可保证全局定位结果的精确性及稳定性。

一种状态约束下的抗差自适应联邦滤波算法

充分利用先验约束信息可以提高多传感器组合导航的定位精度和可靠性。将状态约束下的卡尔曼滤波扩展到传统联邦滤波中,提出一种状态约束下的联邦滤波算法。当子传感器出现异常时,在状态约束下的联邦滤波基础上,采用Huber方法调整子滤波器观测噪声矩阵,同时在信息分配阶段引入自适应信息分配因子,实时调整子滤波器融合权重,得到一种状态约束下的抗差自适应联邦滤波算法,以进一步减少不准确的子滤波器估计对融合结果的影响。将该方法应用在捷联惯导、全球导航卫星系统和里程计的多传感器组合导航系统中。仿真实验表明,状态约束下的联邦滤波估计精度优于传统联邦滤波,状态约束下的抗差自适应联邦滤波能够进一步提高观测异常下的导航定位精度和可靠性。

基于改进的联邦UKF无人艇组合导航系统设计

针对无人艇在高海况下长航时,大幅度作业滤波精度较低的问题,提出一种基于联邦结构的无迹卡尔曼滤波(Unscented Kalman Filtering,UKF)算法,将其应用于自主研制的无人艇组合导航系统中。建立系统误差方程和量测方程;引入渐消因子、基于量测值与预测量测值差值的可变因子和自适应最优信息分配因子对联邦UKF算法进行改进,保持信息的强跟踪特性和组合导航系统的信息融合精度,得到全局最优估计值。开展湖试试验,验证该组合导航系统的有效性,结果表明该系统实时性、稳定性好,抗干扰能力强,能有效提高导航精度。该方法不仅能为无人艇作业提供安全保障,而且可供其他组合导航系统设计参考。

基于马氏距离的极区容错联邦滤波算法

针对舰船组合导航系统受极区极端恶劣环境影响发生外部量测故障从而降低系统精度的问题,提出基于马氏距离的极区容错联邦滤波算法。通过引入真实量测与预测量测之间的马氏距离来衡量各个子滤波器的量测质量,并通过马氏距离来构建各个子滤波器在主滤波器最优融合中对应的融合系数,降低故障子滤波器在最优融合中的比例,减少故障子滤波器对全局估计的不良影响。实验仿真表明,该算法相较于传统的联邦滤波算法,尤其在子滤波器量测故障情况下,具有更好的容错性和可靠性。

基于联邦滤波的仿生偏振组合导航系统滤波器设计

该文针对野外地面导航系统受到环境未知、多源干扰的问题,设计具有环境适应能力与抗干扰能力的仿生偏振组合导航系统。首先引入仿生偏振导航技术,组成无重置型联邦滤波结构的仿生偏振/MIMU/GPS组合导航系统,建立各子系统的干扰模型;其次,为提高系统对于动态未知环境的适用性与抗干扰性,利用联邦滤波器优越的容错能力,设计系统针对几种组合模式的智能切换方案,有效检测并隔离GPS干扰子系统,实现可用系统的重构。

GPS-RTK土地测量技术优化与测量方案创新设计研究

传统的土地测量方法往往受到地理环境、复杂地形和测量精度等问题的限制,因此需要引进更加先进和高效的测量技术。为了提高土地测量能力,引入土地测量工作中的GPS-RTK测量技术,通过多方技术改进以提高土地测量工作的效率和精准度。采用GPS-RTK技术,通过基准站、移动站和全球定位系统(GPS)卫星之间收发信号,可以在移动站实时计算出三者之间的距离,提高土地测量工作的便捷性。在信号处理方面,采用了联邦卡尔曼滤波算法,提高了土地测量工作的信号传播效率。试验结果表明,设计的土地测量工作方案的测绘性能有显著提高。

面向组合惯性导航系统的χ^(2)故障检测与诊断

文章主要对组合导航系统中的χ^(2)故障及其诊断进行研究。将该系统基本的误差模型作为依据,针对其故障诊断,提出了在联邦滤波结构中适用的故障监测技术,即通过联邦滤波结构中的残差检验技术来检测该系统里的硬故障,将没有故障情况下的最优全局估计信息构造滑动残差用作检验标准,来检测该系统中的软故障,并将软故障检测函数作为依据,自适应调节信息分配系数。经实验发现,本次所提出的算法在此类故障检测及其诊断中十分适用。

多惯导系统组合导航下的卡尔曼滤波算法研究

针对捷联式惯性导航系统(简称“惯导系统”)中的卡尔曼滤波算法进行研究。依据惯性导航系统的基本原理建立误差模型,分析多个惯性导航系统在组合导航时的误差特性,将Sage-Husa自适应形式的滤波作为基础,提出了一种多惯导形式的数据融合计算方法。将Sage-Husa自适应形式的滤波算法优势予以保留,结合常规形式的卡尔曼滤波算法,对Sage-Husa滤波进行简化,只对量测噪声进行自适应调节。实验表明,文中的数据融合算法能很好地实现多惯导导航系统的组合导航,融合后的误差估计结果与独立误差趋势一致,且受误差较大惯性导航的影响较小。

MPOS联邦实时组合算法

微小型位置姿态测量系统(micro position and orientation system,MPOS)是为成像载荷提供实时高精度运动补偿信息的关键装置,其测量精度严重制约成像精度的提升。针对MPOS集中滤波实时性差的问题,在基于双ARM(advanced reduced instruction set computing machines)硬件平台的基础上,采用联邦滤波实时组合算法对多组传感器数据进行最优信息融合。以微惯性测量单元为公共参考系统,双天线全球导航卫星系统(global navigation satellite system,GNSS)和磁强计作为子系统提供量测信息,将各个子系统得到的局部误差协方差及状态估计值在主滤波器中进行信息融合得到最优估计值。通过动态实验验证了基于联邦滤波实时组合方法的MPOS,其位置、速度、航向角及水平姿态角精度可达0.6 m,0.03 m/s,0.15°,0.04°。

基于鲁棒AFCKF的无人车多源组合导航方法

针对无人车进行多源信息融合时部分传感器失效或量测异常导致定位精度下降甚至无法定位的问题,提出了一种鲁棒自适应联邦容积卡尔曼滤波(AFCKF)算法。首先,在子滤波器中将Huber方法与容积卡尔曼滤波相结合,并基于马氏距离实时调整Huber方法中的调节因子,提高了子滤波器的估计精度和鲁棒性。其次,在信息融合中,基于预测状态残差以及量测残差的二次型引入一种自适应信息分享因子计算方法,减小了子滤波器不准确估计对主滤波器信息融合的影响。最后,对所提方法进行了仿真实验。仿真实验结果表明,相比于传统的联邦容积卡尔曼滤波和基于Huber方法的联邦容积卡尔曼滤波,所提方法的平均定位精度分别提高了77.84%和17.20%,平均速度精度分别提高了63.25%和9.30%。

随钻测量钻具重力加速度提取方法

针对随钻测量(MWD)中钻具重力加速度受振动干扰大引起的井斜角解算严重失真问题,提出基于联邦自适应无迹卡尔曼滤波(FAUKF)的钻具重力加速度提取方法。首先,建立无重置结构的联邦重力信息融合提取框架,选取基于陀螺仪数据的递推重力值作为联邦滤波的公共参考值,分别与解耦的地磁数据观测的重力加速度组合作为子滤波器1,与加速度计数据观测的重力加速度组合作为子滤波器2。然后,对子滤波器的重力状态进行无迹卡尔曼滤波(UKF)算法,期间根据地磁数据观测重力值的抗振性优于加速计的特性,设计子滤波器2对应的抗振因子,再根据陀螺仪短时精度高的输出特性,找到基于陀螺仪数据解耦的地磁参考斜率值,来设计子滤波器1对应的抗磁因子,提升子滤波器性能,接着用自适应开窗因子来确定开窗估计法则中的开窗值,调节新息协方差,通过对新息协方差的估计实现子滤波器量测噪声协方差的实时估计,提高无迹卡尔曼滤波算法精度,进而得到可靠的重力信息局部自适应估计值。最后通过联邦信息融合,进一步得到重力信息的全局估计。通过模拟钻进和实钻实验结果表明,FAUKF算法和FKF算法相比,井斜角误差减小了±1.9°,FAUKF算法下的井斜角误差可控制在±1.2°以内。该方法可有效提取煤矿井下钻具重力加速度,提高井斜测量精度,是获得可靠钻具井斜角的有效方法。

基于纵横向多速度融合的高速列车测速精度研究

针对现有高速列车速度控制多数存在的测速误差大、运行效率偏低等问题,提出一种纵横向多速度融合测速方法.首先,通过叠加式采样读取4路速度传感器的测速数据,采用联邦卡尔曼滤波算法对4路测速值分别进行纵向融合滤波,并采用衰减记忆法优化该算法存在的滤波发散问题,得到4路纵向融合速度值.其次,对4路纵向融合速度值进行置信距离可靠性判别,确定有效融合速度值数量,排除传感器故障影响.再次,采用优化的贝叶斯数据融合算法对有效纵向融合速度值进行横向多速度融合.最后,对所提算法进行仿真,完成对仿真结果的分析和比较.研究结果表明:基于衰减记忆法的联邦卡尔曼滤波算法的纵向融合速度平均误差为0.6696 km/h,纵横向多速度融合方法的融合速度平均误差为0.3928 km/h,平均测速精度有较大的提升.

复杂环境下无人车双重自适应定位算法

针对复杂环境中部分传感器失效情况下无人车定位精度下降甚至出现无法定位问题,提出局部自适应滤波和自适应信息共享因子分配的双重自适应算法,提升了无人车多源融合定位性能。所提算法利用新息自适应调节测量噪声对局部滤波器进行自适应状态估计,利用局部滤波器状态协方差和观测噪声自适应分配信息共享因子,能有效地抵御观测值粗差影响。仿真结果和实车测试数据均表明,该双重自适应定位算法能够根据局部滤波器新息和状态协方差在线自动更新信息共享因子,相比自适应联邦卡尔曼滤波算法和经典联邦卡尔曼滤波算法,位置精度分别提高了44%和63%,误差小于1 m,验证了所提算法在复杂环境下能有效提高整个导航系统的可靠性、鲁棒性。