基于性能导航的自主完好性监测分析

为满足基于性能的导航(PBN)中所需导航性能(RNP)规范对导航性能监测与告警的要求,设计了基于滤波新息的残差检验法(RCTM)和自主完好性监测外推法(AIME)来进行完好性检测。通过对阶跃故障检测和缓变故障检测的仿真分析,仿真结果表明,RCTM法对于阶跃故障和快变的斜坡故障检测是非常有效的,而AIME法对于慢变的斜坡故障检测具有更好的性能,采用两种方法进行组合的方式,能够提高PBN运行下自主完好性的监测能力。     

基于双状态卡方故障检测的组合导航系统

故障检验与隔离是利用联邦滤波实现组合导航系统安全可靠运行的关键技术。针对常规残差卡方检验法对于慢变故障检验效果差,以及状态卡方检验受到初值误差、系统噪声和建模误差影响导致子系统被频繁隔离的问题,提出一种改进的SINS/GPS/ADS/DVL组合导航系统故障检验算法。该算法采用2个状态推进器交替地对滤波数据进行重置,避免状态递推器被未检测出来的故障污染。实验结果表明,改进的算法能够有效提高故障检验的灵敏度,增强组合导航系统的可靠性。     

JIDS/SINS/GPS组合导航系统两级故障检测结构设计

针对传统组合导航系统故障检测方法不能同时准确检测突变故障及缓变故障的不足,提出了一种两级故障检测结构。这种结构采用联邦滤波器结合了残差χ2检测法易于检测突变故障和状态χ2检测法易于检测缓变故障的优势,在JIDS/SINS/GPS组合导航系统中得到了成功应用。仿真表明,这种方法对突变故障和缓变故障都达到了较好的检测效果。此外,此方法没有改变联邦滤波器原有结构,检测准确度高,计算量小,是一种便于工程实现的检测方法。     

基于预滤波器和两级AIME的GNSS/INS超紧组合慢变故障检测

针对包含预滤波器的GNSS/INS非相干超紧组合架构,提出了一种基于预滤波器的两级AIME慢变故障检测方法。该方法首先基于预滤波器构建第1级AIME检测,并设计了关于第1级AIME检测统计量的检测量Kalman滤波器。在发生慢变故障时,第1级AIME故障检测统计量存在递增趋势。对于检测量Kalman滤波器而言,这种递增趋势也可视作一种慢变故障。以检测量Kalman滤波器为基础,构建了第2级AIME检测算法,以达到减小故障检测时间的目的。在单星和两星伪距慢变故障场景下,进行了仿真与对比分析。仿真结果表明,所提方法能够正确识别故障卫星并且可以显著减小慢变故障的检测时间。对于小变化率的慢变故障,所提方法在检测时间上的优势更加明显。     

卫星导航接收机自主完好性监测算法研究

针对航空导航定位高可靠性的要求和GPS接收机观测噪声分布的特点,研究将粒子滤波算法应用于接收机自主完好性监测（RUM）中.通过粒子滤波算法对状态进行精确估计,利用对数似然比建立一致性检验统计量进行故障检测与隔离.对算法进行了数学建模,描述了完整的RAIM算法详细流程.通过实测数据对提出的RAIM算法进行验证,结果表明：粒子滤波算法在非高斯测量噪声情况下可以对GPS接收机状态进行精确的估计,利用对数似然比建立的一致性检验统计量能有效地检测并隔离故障卫星,验证了该算法应用于GPS接收机自主完好性监测的可行性和有效性.     

INS/GPS组合导航系统故障检测问题研究

提出了一种新的故障检测方案。该方案由卡尔曼滤波 ,得到新息序列 ,利用大数定律对新息序列的稳定性进行检验 ,来判别系统是否出现了故障。同时提出了故障状态下卡尔曼滤波的修正算法。数值仿真结果表明 ,该方法能有效地检测出 INS/GPS组合导航系统的故障 ,保证了 INS/GPS组合导航系统的安全性和可靠性     

基于交互多模估计策略的故障检诊方法

针对多模自适应（ＭＭＡＥ）故障检诊（ＦＤＤ）方法的局限性,提出了一种基于交互多模（ＩＭＭ）估计策略的动态系统中多重故障的检诊方法。交互多模估计是针对包含有结构以及参数的系统的一种效率较好的自适应估计技术,它提供了故障检测、诊断和状态估计的集中框架。通过对在传感器和作动器中含有多个故障飞机的仿真。结果表明,所提供的方法比其它方法能够更快、更可靠地检测和隔离出多重故障。     

改进的状态χ2故障检验方法及其应用

针对单状态递推器存在误差时间累积的缺点,在SINS/GPS组合导航系统传感器故障检测过程中,基于状态χ2检验方法提出了以速度误差作为更新依据的状态递推器更新判断准则,并与双状态χ2检验方法进行了比较验证。仿真结果表明,该方法大大减少了状态递推器受故障污染的几率,同时有效提高了故障检测的灵敏度。     

改进的状态X^2故障检验方法及其应用

针对单状态递推器存在误差时间累积的缺点，在SINS／GPS组合导航系统传感器故障检测过程中，基于状态X^2检验方法提出了以速度误差作为更新依据的状态递推器更新判断准则，并与双状态X^2检验方法进行了比较验证。仿真结果表明，该方法大大减少了状态递推器受故障污染的几率，同时有效提高了故障检测的灵敏度。     

运载火箭故障检测处理系统软件研究和实践

介绍载人运载火箭故障检测处理系统软件的主要情况、重要故障模式和特点,从软件研制过程、配置管理、技术状态控制、软件测试、可靠性安全性等方面总结了故障检测处理系统软件工程化的经验,为下一步任务提供参考与借鉴。     

One-step solution for the multistep out-of-sequence-measurement problem in tracking

In multisensor target tracking systems measurements from the same target can arrive out of sequence. Such "out-of-sequence" measurement (OOSM) arrivals can occur even in the absence of scan/frame communication time delays. The resulting problem - how to update the current state estimate with an "older" measurement - is a nonstandard estimation problem. It was solved first (suboptimally, then optimally) for the case where the OOSM lies between the two last measurements, i.e, its lag is less than a sampling interval - the 1-step-lag case. The real world has, however, OOSMs with arbitrary lag. Subsequently, the suboptimal algorithm was extended to the case of an arbitrary (multistep) lag, but the resulting algorithm required a significant amount of storage. The present work shows how the 1-step-lag algorithms can be generalized to handle an arbitrary (multistep) lag while preserving their main feature of solving the update problem without iterating. This leads only to a very small (a few percent) degradation of MSE performance. The incorporation of an OOSM into the data association process is also discussed. A realistic example with two GMTI radars is presented. The consistency of the proposed algorithm is also evaluated and it is found that its calculated covariances are reliable.     

Removal of Out-of-Sequence Measurements from Tracks

In multisensor tracking systems that operate in a centralized or distributed information processing architecture, measurements from the same target obtained by different sensors can arrive at the processing center out of sequence due to system latencies. In order to avoid either a delay in the output or the need for reordering and reprocessing entire sequences of measurements, such latent measurements have to be processed by the tracking filter as out-of-sequence measurements (OOSM). Recent work developed a "one-step" procedure for incorporating OOSM with multiple-time-step latency into the tracking filter, which, while suboptimal, was shown to yield results very close to those obtained by reordering and reprocessing an entire sequence of measurements. The counterpart of this problem is the need to remove (revocate) measurements that have already been used to update a track state. This can happen in real-world systems when such measurements are reassigned to another track. Similarly to the problem of update with an OOSM, it is desired to carry out the removal of an earlier measurement without recomputing the track estimate (and the data association) using possibly a long sequence of subsequent measurements one at a time. A one-step algorithm is presented for this problem of removing a multistep OOSM.     

Mesh deformation on 3D complex configurations using multistep radial basis functions interpolation

The Radial Basis Function (RBF) method with data reduction is an effective way to perform mesh deformation. However, for large deformations on meshes of complex aerodynamic configurations, the efficiency of the RBF mesh deformation method still needs to be further improved to fulfill the demand of practical application. To achieve this goal, a multistep RBF method based on a multilevel subspace RBF algorithm is presented to further improve the efficiency of the mesh deformation method in this research. A whole deformation is divided into a series of steps, and the supporting radius is adjusted in accordance with the maximal displacement error. Furthermore, parallel computing is applied to the interpolation to enhance the efficiency. Typical deformation problems of the NASA Common Research Model (CRM) configuration, the DLR-F6 wing-body-nacelle-pylon configuration, and the DLR-F11 high-lift configuration are tested to verify the feasibility of this method. Test results show that the presented multistep RBF mesh deformation method is efficient and robust in dealing with large deformation problems over complex geometries.     

一类非平稳随机过程未来变化趋势的多步实时预报

本文在回避建立过程加速度模型这一传统方法的前提下，提出了一类非平衡随机过程未来变化趋势的多步实时预报模型，并对高斯白噪声背景下过程变化的共性进行了研究。文中给出了算例与仿真计算结果。     

Multi-criteria analysis of aircraft structures fracture

A novel modular multistep algorithm is proposed for multi-criteria analysis of fracture of aircraft structures with complex geometries. In order to evaluate the algorithm, the crack growth analysis is realized by the following three criteria, namely, the stress intensity factor, the strain energy release rate, and the J-integral. The algorithm implementation by the finite element method is considered and prospects of alternative implementation based on the meshfree methods are assigned.     

涡轴发动机自适应非线性预测控制

为了实现当直升机旋翼负载变化时,尽量保持功率涡轮转速恒定,并提高系统动态品质,研究了一种针对涡轴发动机的自适应非线性预测控制(ANMPC)算法.基于涡轴发动机稳态数据和动态特性,采用递归最小二乘法(RLS)进行模型参数辨识,建立了具有在线自适应能力的涡轴发动机数值-ARX(auto regressive with external input)并联预测模型.在此基础上,通过多步输出预测和反馈校正,利用序列二次型优化(SQP)算法,进行在线滚动优化,从而获得了涡轴发动机ANMPC控制器.仿真结果表明:当旋翼负载变化时,相比于传统的串联PID(比例-积分-控制)控制器,ANMPC控制器能够使得功率涡轮转速收敛更快,超调量/下垂量更小.      

ARMAX模型参数估计的新两步法

 本文提出了一种ARMAX模型参数估计的新两步法。这种方法与其它递推估计方法（如增广矩阵法,极大似然法,Durbin两步法、三步法等）比较,具有较好的收敛特性,对参数的估计,特别是对C参数的估计比较精确,这是利用其它方法,一直未能很好解决的一个难题。     

模型预测控制在永磁同步电机系统中的应用发展综述

随着数字技术的发展,模型预测控制(MPC)已经广泛应用于交流传动系统。首先介绍了经典MPC策略——有限控制集MPC和连续控制集MPC的原理。其次综述了多步预测控制、多矢量预测控制、具有参数鲁棒性的预测控制、广义模型预测、显式模型预测这些常见的改进MPC的研究现状,并提出相关研究思路。最后,根据MPC的应用需求和研究现状,展望了未来需要进一步深入和拓展的研究方向。     

基于GJB5000A三级的软件配置管理技术研究

软件配置管理是软件工程的一个重要领域.配置管理的目的是利用配置标识、配置控制、配置状态纪实和配置审核建立和维护工作产品的完整性.有效的软件配置管理活动有助于软件质量的提高.对在GJB5000A研发过程的配置管理技术进行充分研究后,经大量实践,提出一种基于GJB5000A三级标准的软件配置管理解决方案,并将其与GJB5000A进行对比,提供符合性说明.     

A dimension reduced INS/VNS integrated navigation method for planetary rovers

Inertial navigation system/visual navigation system (INS/VNS) integrated navigation is a commonly used autonomous navigation method for planetary rovers. Since visual measurements are related to the previous and current state vectors (position and attitude) of planetary rovers, the performance of the Kalman filter (KF) will be challenged by the time-correlation problem. A state augmentation method, which augments the previous state value to the state vector, is commonly used when dealing with this problem. However, the augmenting of state dimensions will result in an increase in computation load. In this paper, a state dimension reduced INS/VNS integrated nav-igation method based on coordinates of feature points is presented that utilizes the information obtained through INS/VNS integrated navigation at a previous moment to overcome the time rel-evance problem and reduce the dimensions of the state vector. Equations of extended Kalman filter (EKF) are used to demonstrate the equivalence of calculated results between the proposed method and traditional state augmented methods. Results of simulation and experimentation indicate that this method has less computational load but similar accuracy when compared with traditional methods.