Highly reliable relative navigation for multi-UAV formation flight in urban environments

Formation flight of multiple Unmanned Aerial Vehicles (UAVs) is expected to bring significant benefits to a wide range of applications. Accurate and reliable relative position information is a prerequisite to safely maintain a fairly close distance between UAVs and to achieve inner-system collision avoidance. However, Global Navigation Satellite System (GNSS) measurements are vulnerable to erroneous signals in urban canyons, which could potentially lead to catastrophic consequences. Accordingly, on the basis of performing relative positioning with double differenced pseudoranges, this paper develops an integrity monitoring framework to improve navigation integrity (a measure of reliability) in urban environments. On the one hand, this framework includes a fault detection and exclusion scheme to protect against measurement faults. To accommodate urban scenarios, spatial dependence in the faults are taken into consideration by this scheme. On the other hand, relative protection level is rigorously derived to describe the probabilistic error bound of the navigation output. This indicator can be used to evaluate collision risk and to warn collision danger in real time. The proposed algorithms are validated by both simulations and flight experiments. Simulation results quantitatively reveal the sensitivity of navigation performance to receiver configurations and environmental conditions. And experimental results suggest high efficiency and effectiveness of the new integrity monitoring framework.     

基于VBGMM-DCNN的列车卫星定位欺骗干扰检测方法

面向基于全球导航卫星系统的铁路列车定位实施欺骗干扰的主动检测，在卫星定位解算层次，运用深度学习建模学习方法的优势，提出一种基于变分贝叶斯高斯混合模型-深度卷积神经网络（variational Bayesian Gaussian mixture model-deep convolutional neural network, VBGMM-DCNN）的列车卫星定位欺骗干扰检测方法。该方法首先提取能够充分体现欺骗干扰对定位解算过程作用影响的卫星观测特征参数，构建干扰检测特征矢量；然后，采用VBGMM模型拟合经过预处理的特征向量的概率分布，得到二维概率密度图；最后，将概率密度图用于DCNN模型实施欺骗干扰的检测决策。结合现场实验所得运行场景数据，利用实验室搭建的欺骗干扰测试环境实施了干扰注入测试与检验，结果表明,欺骗干扰检测性能随着DCNN网络深度的增加而提升，相对于常规有监督决策方法F1值最高提升44.68%。基于VBGMM-DCNN的欺骗干扰检测能够适应测试验证中运用的列车运行特征及定位观测条件，所达到的检测性能优于对比算法。     

区域导航无人机空中基准站的多源信息融合容错导航系统研究

作为卫星导航系统的补充和备份,区域导航服务系统近年来得到较大发展。在基于无人机的区域导航服务系统中,无人机自身的定位精度对区域导航服务系统的可靠运行有直接的影响。针对无人机导航传感器及系统的容错和可靠性问题,设计了具有针对性和自优化功能的多源信息融合容错导航方案,提出了一种优化的基于矢量分配形式的自适应联邦滤波算法。通过对每个状态量设计不同的信息分配系数,实现传感器量测噪声的动态优化调整,有效减小了传感器故障对融合导航系统的影响,提高了无人机导航系统的鲁棒性。验证分析表明,该方法可以减小子滤波器故障信息对融合导航系统联邦滤波全局估计的影响,避免了故障子滤波器在信息重置过程中对系统造成的污染,提高和保障了无人机空中基准站多源信息融合导航系统的稳定性和可靠性。     

复杂城市环境下GNSS/INS轻轨轨道中线精密测量方法CSCD

在城市轻轨线路的中线精密测量中,传统的基于GNSS的测量方式往往受高楼、站台、上跨桥及声屏障等复杂环境信号遮挡影响,甚至无法接收到GNSS信号,难以得到满足精度要求的轨道中线坐标;而基于全站仪的测量方式需建立轨道控制网,作业效率低下。为了满足城市轻轨中线精密测量高精度和高效率的需求,采用GNSS/INS轨道中线精密测量方法,基于Kalman滤波将惯导、GNSS数据进行融合,并根据轨道检测仪在轨道上不发生垂向和侧向运动的特点,采用运动约束算法,以提升组合导航系统测量精度。同时设计了基于新息滤波的抗差检测,以剔除定位质量差的GNSS数据。通过将里程计比例因子误差增广到状态向量的方式进行参数估计,以减小里程计测量误差。通过集成惯导、GNSS、里程计等传感器的轨检小车,采集城市轻轨轨道测量数据。经数据处理和对比分析,在GNSS信号较差的复杂场景下能够得到厘米级的轻轨轨道中线坐标,且测量效率大大提升。     

直升机旋翼状态监测和故障诊断试验研究

直升机旋翼系统故障诊断在直升机故障诊断中占有很重要的地位。本文采用试验方法及BP神经网络完成了旋翼故障识别和程度识别，为我国直升机旋翼诊断和神经网络在直升机故障诊断中的应用打下一定基础。     

基于场景检测的城市环境GNSS/INS组合定位方法研究

城市复杂环境下,采用GNSS/INS组合定位方法能有效提升GNSS信号部分或全部被遮挡情况下的车辆连续定位能力.GNSS信号受遮挡的程度具有随机性和突变性,给GNSS/INS组合算法的架构设计和参数设置带来了挑战.基于此,设计了一套基于场景检测的GNSS/INS组合定位策略与方法,提出了以多星GNSS观测量的八类特征为...     

基于自联想网络的发动机传感器解析余度技术

本文提出了一种基于自联想神经网络的传感器解析余度技术。在这种网络中,冗余传感器的信息被压缩、重组进入网络的第一部分,网络的第二部分将压缩信息恢复出来。基于数据融合原理,若一个传感器发生故障,其它传感器仍可提供足够的信息代替发生故障的传感器。理论分析和用于涡轴发动机的仿真结果表明,这种特殊结构的自联想网络具有良好的过滤噪声和故障信号的作用,特别适合于用作不易建模的复杂对象的传感器信号重构     

密林环境空地协同GNSS/UWB快速高精度定位技术

针对密林中卫星信号遮挡难以实现快速高精度定位的问题,提出了一种密林环境空地协同全球卫星导航系统/超宽带(GNSS/UWB)高精度定位方法。该方法综合考虑无人机平台在空旷环境快速运动与超宽带的强穿透性测量等特征,通过无人机携带GNSS/UWB集成化载荷,以移动单基站模拟多基站,配合密林中UWB标签组网测距,完成密林中UWB标签定位。对基站布设方案展开研究,为密林环境下空地协同GNSS/UWB快速高精度定位技术应用提供基站布设指导和依据,并且利用密林环境下实测测距实验+仿真定位实验对提出的方法进行验证。结果表明,所述方法可有效实现密林环境下的快速高精度定位,精度达到分米级,并且通过优选基站布设网型、范围及高度可有效提高定位精度,为密林环境下快速高精度定位方法提供了理论支撑。     

Detection and diagnosis of sensor and actuator failures using IMMestimator

An approach to detection and diagnosis of multiple failures in a dynamic system is proposed. It is based on the interacting multiple-model (IMM) estimation algorithm, which is one of the most cost-effective adaptive estimation techniques for systems involving structural as well as parametric changes. The proposed approach provides an integrated framework for fault detection, diagnosis, and state estimation. It is able to detect and isolate multiple faults substantially more quickly and more reliably than many existing approaches. Its superiority is illustrated in two aircraft examples for single and double faults of both sensors and actuators, in the forms of “total”, “partial”, and simultaneous failures. Both deterministic and random fault scenarios are designed and used for testing and comparing the performance fairly. Some new performance indices are presented. The robustness of the proposed approach to the design of model transition probabilities, fault modeling errors, and the uncertainties of noise statistics are also evaluated     

液体火箭发动机启动过程实时在线故障检测算法

利用神经网络技术实现了液体火箭发动机启动过程的非线性辩识；提出并实现了一种基于辩识误差检验的故障检测策略。经大量实际发动机热试车数据验证表明，所提出的检测算法十分有效。由于算法所利用的监测参数均系实际发动机地面试车中所测量的参数，且检测算法在线工作时计算量十分小，因而所提出并实现的检测算法可以直接应用于工程实际。     

面向Android智能终端的多模GNSS实时非差精密定位

Android操作系统中全球导航卫星系统(GNSS)原始数据的开放为大众高精度位置服务的应用带来了重要机遇。在对Android系统GNSS原始数据特性分析的基础上,利用智能终端GNSS原始数据实现了实时非差精密定位,研制了面向Android平台的实时精密单点定位(PPP)软件PPPAnd,并开展了实际环境下的定位测试。测试结果表明:基于Android终端GNSS原始数据的实时静态伪距单点定位精度(RMS)为1.16m(水平方向)和1.51m(垂直方向),较其自身位置速度和时间(PVT)解算结果分别提高了70%和76%;实时静态精密单点定位解算结果的精度(RMS)为0.62m(水平方向)和0.66m(垂直方向),较PVT结果分别提高了87%和82%,精度收敛至1m以内所需时间约8min,并且收敛后的精度可达亚米级;城市环境中车载实时动态精密单点定位的水平和垂直精度(RMS)分别约为1.32m和0.81m,较PVT结果分别提高了39%和65%。     

基于惯性辅助的北斗接收机完好性监测方法研究

针对传统卫星导航接收机在卫星数小于5颗及卫星星座构型不佳的情况下无法实现卫星故障识别与隔离的问题,结合卫星导航接收机在弹上的实际应用情况,利用惯导的辅助数据设计了一种基于多级Kalman滤波的北斗接收机完好性监测方案,给出了卫星导航接收机的RAIM滤波器结构与故障检测和隔离方法,并对其可用性进行了推导.该方案经过仿真分...     

GJB289A总线网络电气特性仿真及故障分析

机载GJB289A 总线用于实现各类航空电子设备之间的物理连接,其可靠性直接影响着飞机航电系统的正常运行。为了分析GJB289A 总线网络典型电气故障特性,根据GJB289A 总线网络变压器、终端电阻分布参数及有损耦合传输线模型,在ADS（Advanced Design System）软件中构建包含两个耦合变压器的两端...     

Real-time fault detection method based on belief rule base for aircraft navigation system

Real-time and accurate fault detection is essential to enhance the aircraft navigation system’s reliability and safety. The existent detection methods based on analytical model draws back at simultaneously detecting gradual and sudden faults. On account of this reason, we propose an online detection solution based on non-analytical model. In this article, the navigation system fault detection model is established based on belief rule base (BRB), where the system measuring residual and its changing rate are used as the inputs of BRB model and the fault detection function as the output. To overcome the drawbacks of current parameter optimization algorithms for BRB and achieve online update, a parameter recursive estimation algorithm is presented for online BRB detection model based on expectation maximization (EM) algorithm. Furthermore, the proposed method is verified by navigation experiment. Experimental results show that the proposed method is able to effectively realize online parameter evaluation in navigation system fault detection model. The output of the detection model can track the fault state very well, and the faults can be diagnosed in real time and accurately. In addition, the detection ability, especially in the probability of false detection, is superior to offline optimization method, and thus the system reliability has great improvement.     

部分模糊度固定方法在GNSS/INS紧组合中的应用研究

针对动态环境下GNSS/INS导航定位模糊度固定难的问题,提出了一种基于部分模糊度固定的GNSS/INS紧组合导航定位算法,通过选择模糊度的最优子集进行固定,并利用实测车载实验进行了算法验证.实验结果表明:在开阔环境下,基于部分模糊度固定的GNSS/INS紧组合与基于全模糊度固定的GNSS/INS紧组合算法的定位精度相...     

Fault diagnostics and fault tolerant control

A novel simultaneous fault detection and diagnostics (FDD) and fault tolerant control (FTC) strategy for nonlinear stochastic systems in closed loops based on a continuous stirred tank reactor (CSTR) is presented. The purpose of control is to track the reactant concentration setpoint. Instead of output feedback we propose here to use proportional-integral-derivative (PID) state feedback, which is shown essential to achieve FTC against sensor faults. A new concept of “equivalent bias” is proposed to model the sensor faults. Both the states and the equivalent bias are on-line estimated by a pseudo separate-bias estimation algorithm. The estimated equivalent bias is then evaluated via a modified Bayes' classification based algorithm to detect and diagnose the sensor faults. Many kinds of sensor faults are tested by Monte Carlo simulations, which demonstrate that the proposed strategy has definite fault tolerant ability against sensor faults, moreover the sensor faults can be on-line detected, isolated, and estimated simultaneously     

广义回归神经网络在冗余捷联惯导故障诊断中的应用研究

针对冗余捷联惯导的故障诊断问题，研究提出了一种基于广义回归神经网络的故障诊断方法。该方法在传感器输出数学模型未知的情况下，仅通过传感器之间的冗余关系，利用传感器正常工作时的测量值和改进的神经网络估计输出值生成残差进行故障诊断。仿真试验表明，利用神经网络补偿产生的残差可以检测到未补偿时的故障。该方法不仅可以检测到单故障，还对多传感器同时发生故障具有一定的检测能力。     

Robust fault detection and optimization for a network of unmanned vehicles with imperfect communication channels

The observer-based robust fault detection and optimization for a network of unmanned vehicles with imperfect communication channels and norm bounded modeling uncertainties are addressed. The network of unmanned vehicles is modeled as a discrete-time uncertain Markovian jump system. Based on the model, a residual generator is constructed and the sufficient condition for the existence of the desired fault detection filter is derived in terms of linear matrix inequality. Furthermore, a time domain optimization approach is proposed to improve the performance of the fault detection system. The problem of detecting small faults can be formulated as an optimization problem and its solution is given. For preventing false alarms, a new adaptive threshold function is established. The combined fault detection and optimization algorithm and the adaptive threshold are then applied to a network of highly maneuverable technology vehicles to illustrate the effective- ness of the orooosed aooroach.     

User-level reliability monitoring in urban personal satellite-navigation

Monitoring the reliability of the obtained user position is of great importance, especially when using the global positioning system (GPS) as a standalone system. In the work presented here, we discuss reliability testing, reliability enhancement, and quality control for global navigation satellite system (GNSS) positioning. Reliability testing usually relies on statistical tests for receiver autonomous integrity monitoring (RAIM) and fault detection and exclusion (FDE). It is here extended by including an assessment of the redundancy and the geometry of the obtained user position solution. The reliability enhancement discussed here includes rejection of possible outliers, and the use of a robust estimator, namely a modified Danish method. We draw special attention to navigation applications in degraded signal-environments such as indoors where typically multiple errors occur simultaneously. The results of applying the discussed methods to high-sensitivity GPS data from an indoor experiment demonstrate that weighted estimation, FDE, and quality control yield a significant improvement in reliability and accuracy. The accuracy actually obtained was by 40% better than with equal weights and no FDE; the rms value of horizontal errors was reduced from 15 m to 9 m, and the maximum horizontal errors were largely reduced.     

基于数据关联的故障快速检测

 多数情况下,快速实时地进行故障检测是很重要的。将故障看做是通过多传感器观测的动态模型,进行多传感器多模型概率数据关联,以各个模型的关联结果和设定的阈值为依据,可以有效地实现故障检测。联合概率数据关联（JPDA）算法是解决多传感器多目标跟踪的一个有效方法,文中通过分析概率数据关联算法,对联合概率数据关联算法进行了改进：（1）通过正确地选择阈值,移除小概率事件,进而建立一个近似的确认矩阵;（2）根据被跟踪目标故障跟踪门的相交情况,将跟踪空间进行数学划分,形成若干相互独立的区域;（3）对同一区域内公共有效量测的概率密度值进行衰减,计算出关联概率。仿真对比表明,本文的改进算法能显著减少计算时间,有效提高故障检测的快速性和实时性。