捷联惯导/北斗高精度组合导航方法研究

提出采用紧组合方式进行捷联惯导/北斗组合导航设计,首先对捷联惯导与北斗系统进行误差分析与建模,将捷联惯导系统误差、北斗等效时钟误差相应的距离(伪距误差)以及等效时钟频率误差相应的距离率(伪距率误差)作为组合导航系统状态;利用捷联惯导位置输出与北斗接收机星历输出构造获得等效伪距,将其与北斗接收机测量的伪距对应相减作为量测,推导建立对应的量测方程,采用卡尔曼滤波设计捷联惯导/北斗组合导航滤波算法;仿真结果表明,该组合导航方法的速度精度达到±0.05 m/s,位置精度达到±3.2 m,水平姿态精度达到±0.4′,航向精度达到±1.6′。     

车载GPS/光纤陀螺组合导航定姿技术研究

为了实现基于低精度的光纤传感器的高精度定姿导航,以确保陆上运动载体能够得到自身实时、准确的姿态信息,时光纤陀螺捷联惯导系统的测量误差进行了分析并建立组合导航系统的动态模型,利用GPS天线与惯导系统在栽体上的相时安装关系以及陆上载体的运动特征约束即非完整约束建立组合导航系统观测方程,采用卡尔曼滤渡器时惯导系统的测量误差和GPS天线与惯导系统间的杆臂量测误差进行估计,并使用估计结果时惯导系统的速度、姿态导航解算结果予以在线修正.通过车栽试验验证了此组合导航方案切实可行,系统定姿结果具有较高精度.     

车载航位推算组合导航算法研究

对航位推算算法进行了研究,提出了适用于车辆导航的航位推算组合导航算法;该算法在传统的航位推算算去的基础上,利用惯组中加速度计组合的输出和航位推算给出的位置与姿态完成了惯导速度更新;由于惯导速度误差与航位推算速度误差包含有不同的误差信息,将两者做差所得到的速度量测结合位置量测可以在载车具有转弯机动的条件下对航位推算系统的姿态误差、安装误差以及测速设备刻度系数误差进行有效估计,这对于提高航位推算系统的性能是非常有益的;最后通过计算机仿真对该算法的有效性进行了验证.     

车载惯导里程仪组合导航系统安装误差标定研究

研究了捷联惯导、GPS、里程仪和气压高度计构成的组合导航系统中惯导安装误差角对里程仪航位推算精度的影响;提出了以GPS输出作为辅助信息对惯导安装误差进行标定的方法;设计了以里程仪航位推算误差传播方程为系统方程,以里程仪航位推算结果和GSP位置输出之差为量测,通过卡尔曼滤波估计惯导安装误差的标定方法;仿真结果表明,该方法对惯导安装误差的标定精度能达到角秒级。在调试过程中采用该方法标定补偿后的系统实际跑车实验航位推算精度达到5m+行程的0.15%,表明补偿后残余的惯导安装误差影响已经可以忽略。     

基于图像匹配的飞行器导航定位算法及仿真

在飞行器导航定位优化控制的研究中,针对无法获得GPS信号时,长航时飞行器捷联惯导系统存在误差,提出了关于图像地标的飞行器精确定位算法,为实现成像系统对捷联惯导系统的误差校正奠定了基础。利用特定地标在机载摄像机像平面上的图像信息,通过图像变换、匹配定位、坐标变换,解算摄像机空间三维坐标和三个姿态角。再根据摄像机坐标系与机体坐标系的变换关系,推算飞行器精确位置。通过计算机仿真对算法进行了验证,结果表明,提出的算法能够满足GPS不可用时长航的飞行器组合导航精度要求,研究结果对长航时飞行器导航具有一定借鉴和参考价值。     

惯性导航系统在线标定方法研究

论文针对惯性导航系统在线标定的问题,利用GPS提供的基准速度和位置信息,采用基于卡尔曼滤波技术的“速度＋位置”匹配方法,以惯导系统与GPS的速度和位置的差值作为量测值,对加速度计和陀螺仪误差进行在线标定。仿真结果表明,经在线标定补偿后惯导系统定位误差降低了97．2％。该方法显著提高了导航精度,效率较高,具有一定的实用价值。     

捷联惯导在轨标定算法研究

为了保证长期在轨工作的捷联惯性导航系统的精度,设计了一种捷联惯导在轨标定算法.通过分析捷联惯导的主要误差源,建立了标定误差模型和导航误差模型.用加速度计的零位漂移、陀螺仪的零位漂移、陀螺仪的标度因数误差和安装误差构造系统状态方程,利用卫星导航系统提供的速度、位置信息和星敏感器提供的姿态信息构造量测方程,经过滤波运算得到各项误差估计值.算法解决了捷联惯导与星敏感器坐标轴不重合的问题,提高了嵌入式计算机的计算能力.最后对算法进行了数学仿真,仿真结果证明了算法的有效性.     

基于微处理器的紧耦合组合导航系统设计

首先分析了GPS导航与惯性导航各自的优缺点,阐述了GPS/惯导组合导航系统的先进性,其次介绍了众多组合模式中的利用伪距和伪距率信息的紧耦合组合模式,最后基于微处理器从软硬件两方面对组合导航系统进行了设计.     

IMU/GPS/DCM组合导航系统全组合算法研究

通过分析姿态组合算法中平台误差角与姿态误差角物理意义的不同,推导并得到了二者之间的转换关系;利用联邦滤波技术,设计了弹载IMU/GPS/DCM组合导航系统,其中IMU为战术级捷联式惯导系统,GPS为普通民用级;仿真结果表明在元器件精度很低的情况下,使用文中的算法及系统设计依然可以实现高精度导航;组合导航系统的姿态误差角可控制在10角分左右,位置精度在3 m以内,具有较高的实际应用价值。     

组合导航系统在四旋翼无人机上的实现

针对无人飞行器行业的快速发展和导航系统对飞行器的重要性,提出了组合导航系统的融合方案。介绍了捷联惯导系统的原理、姿态算法。通过对惯性传感器进行误差标定和补偿,利用扩展卡尔曼滤波器建立了INS/GPS组合导航系统。仿真实验表明,组合导航系统的工作性能要优于纯惯性导航系统,能够为飞行器提供较高的导航精度。最后,将这种组合导航系统在四旋翼无人飞行器上进行了实现。     

基于IMU阵列的标定方法

微电子机械系统(MEMS)技术的发展使惯性传感器行业发生了革命性的变化,这使得生产惯性传感器阵列成为可能。然而,低成本的惯性测量系统会受到比例因子和轴失准误差的影响,从而造成位置和姿态估计的精度降低。在单个IMU校正的基础上,设计了一套基于IMU阵列的标定方法,该标定方法为了解决传统六面法在标定IMU阵列过程中方向激励不足的问题,设计了正20面的校正装置,该标定方法不仅能够估计出IMU阵列中单个IMU的比例因子、轴失准误差和偏置,还能估计出阵列中不同IMU之间的坐标轴对齐误差。通过把标定结果和官方所给的校正参数进行对比,可以得到经过本文所提的IMU阵列标定方法得到的标定结果能够达到工厂标定结果的百分之五十到百分之九十。     

SINS/CNS组合导航系统陀螺在线标定技术

陀螺仪长时间工作时,由于环境变化等因素,会产生陀螺漂移、标度因数误差和安装轴不正交误差,而且由积分得到的姿态参数信息误差也会相应变大,因此需要对陀螺进行在线标定。借助CNS提供的高精度姿态信息,基于四元数误差建立陀螺在线标定模型,采用卡尔曼滤波器对SINS/CNS组合导航系统陀螺在线标定技术进行仿真研究。仿真结果验证了该算法的有效性,能够估计误差模型中的所有参数,并且满足标定精度要求,具有工程应用价值。     

A 3D indoor positioning system based on low-cost MEMS sensors

A positioning system in the absence of GPS is important in establishing indoor directional guidance and localization. Inertial Measuring Units (IMUs) can be used to detect the movement of a pedestrian. In this paper, we present a three-dimensional (3D) indoor positioning system using foot mounted low cost Micro-Electro-Mechanical System (MEMS) sensors to locate the position and attitude of a person in 3D view, and to plot the path travelled by the person. The sensors include accelerometers, gyroscopes, and a barometer. The pedestrians motion information is collected by accelerometers and gyroscopes to achieve Pedestrian Dead-Reckoning (PDR) which is used to estimate the pedestrian’s rough position. A zero velocity update (ZUPT) algorithm is developed to detect the standing still moment. A Kalman filter is combined with the ZUPT to eliminate non-linear errors in order to obtain accurate positioning information of a pedestrian. The information collected by the barometer is integrated with the accelerometer data to detect the altitude changes and to obtain accurate height information. The main contribution of this research is that the approach proposed fuses barometer and accelerometer in Kalman filter to obtain accurate height information, which has improved the accuracy at x axis and y axis. The proposed system has been tested in several simulated scenarios and real environments. The distance errors are around 1%, and the positioning errors are less than 1% of the total travelled distance. Results indicate that the proposed system performs better than other similar systems using the same low-cost IMUs.     

3-D Unitary ESPRIT: Accurate attitude estimation for unmanned aerial vehicles with a hexagon-shaped ESPAR array

Accurate estimation of the attitude of unmanned aerial vehicles (UAVs) is crucial for their control and displacement. Errors in the attitude estimate may misuse the limited battery energy of UAVs or even cause an accident. For attitude estimation, proprioceptive sensors such as inertial measurement units (IMUs) are widely applied, but they are susceptible to inertial guidance error. With antenna arrays currently being installed in UAVs for communication with ground base stations, we can take advantage of the array structure in order to improve the estimates of IMUs via data fusion. In this paper, we therefore propose an attitude estimation system based on a hexagon-shaped 7-element electronically steerable parasitic antenna radiator (ESPAR) array. The ESPAR array is well-suited for installment in the UAVs with broad wings and short bodies. Our proposed solution returns an estimation for the pitch and roll based on the inter-element phase delay estimates of the line-of-sight path of the impinging signal over the antenna array. By exploiting the parallel and centrosymmetric structure in the hexagon-shaped ESPAR array, the 3-dimensional Unitary ESPRIT algorithm is applied for phase delay estimation to achieve high accuracy as well as computational efficiency. We devise an attitude estimation algorithm by exploiting the geometrical relationship between the UAV attitude and the estimated phase delays. An analytical closed-form expression of the attitude estimates is obtained by solving the established simultaneous nonlinear equations. Simulations results show the feasibility of our proposed solution for different signal-to-noise ratio levels as well as multipath scenarios.     

天基光学传感器姿态实时校正算法

卫星在轨运行过程中,载荷姿态不可避免受到平台抖动、传感器热形变等安装误差因素一定程度的影响,导致图像定位精度下降。针对该问题,通过建立相应的安装误差状态转移模型和基于特征点的观测模型、采用扩展卡尔曼滤波（ EKF）,实现对安装误差的实时估计。采用基于特征点的天基传感器姿态实时校正方法,数学仿真表明：该方法能够实现姿态实时校正,且性能稳定,可有效提高图像定位精度。     

Attitude determination algorithm using state estimation including lever arms between center of gravity and IMU

In this paper, an enhanced attitude determination algorithm is proposed to decrease the estimation error by including an additive state variable for the lever arm. Attitude determination generally is carried out by measurements from an IMU (inertial measurement unit), which is typically located at the center of gravity of the vehicle. The IMU lever arm, which spans the distance between the IMU and the center of gravity, causes extra acceleration in the accelerometer and increases the error in attitude estimates. However, if the extra accelerations caused by the lever arm can be removed from the measurements of accelerometers, the increased attitude error caused by the IMU lever arm can be prevented. Because an IMU lever arm is fixed in a vehicle after installation, it can be considered as an additive element of the state vector in Kalman filter for attitude determination. The proposed algorithm is composed of a quaternion-based Kalman filter and includes an estimation of the IMU lever arm. In addition, in order to determine components of lever arm, the gross measure of modal observability is investigated for the system. An evaluation of the proposed algorithm is carried out by simulations with a noise model based on an actual IMU. Evaluations through simulations show that the proposed algorithm improves the performance with regard to errors.     

Adaptive Fuzzy PD+ Control for Attitude Maneuver of Rigid Spacecraft

In this paper, an adaptive fuzzy PD+ controller is proposed for the attitude maneuver of rigid spacecraft. The novel controller adjusts the gains of the PD+ attitude controller online according to attitude errors and angular velocity errors during the maneuver procedure. Therefore, quick response and avoidance of actuator saturation can be achieved simultaneously. Furthermore, the adaptation mechanism is designed, based on Lyapunov theory, to guarantee the stability of the closed‐loop system. To achieve good performance of the closed‐loop system under the constraint of actuator saturation, controller parameter optimization is developed on the basis of a genetic algorithm. Simulation results show that the transient performance and robustness against parametric uncertainty and environmental disturbance of the adaptive fuzzy PD+ controller are better than those of a constant PD+ controller.     

敏捷凝视卫星密集点目标聚类与最优观测规划

针对敏捷凝视卫星密集点目标观测规划问题,提出一种快速观测任务聚类策略和启发式蚁群优化算法.首先,针对敏捷凝视卫星视场范围特点,提出基于顶点度的团划分算法,解决密集点观测任务聚类问题,形成系列团观测目标,有效提高观测效率;其次,为得到最优团目标观测序列,考虑目标可见时间窗口约束以及卫星敏捷机动能力约束,构建基于多目标观测收益和姿态机动能耗的性能指标,实现能量高效的任务规划;再次,为克服传统蚁群算法易陷入局部极小值和收敛较慢的缺点,设计一种同时考虑目标点优先级、目标可见时间窗口、目标之间卫星姿态转换时间等因素的启发式蚁群算法;最后,选取大规模密集地面目标验证所提出算法的可行性和高效性.     

基于机动检测的捷联航姿算法研究

针对低精度陀螺仪、加速度计和磁传感器组成的捷联航姿系统存在的易受载体运动加速度影响而导致姿态精度下降甚至发散的问题进行了研究,提出了一种基于机动检测的捷联航姿算法。该算法根据陀螺仪数据进行姿态实时更新,利用加速度计和磁传感器输出对载体姿态误差进行校正以保持航姿输出的长期精度。算法根据加速度计输出在导航系中投影的水平分量进行机动检测,剔除机动期间的加速度数据,利用载体匀速运动状态下的加速度数据与磁传感器数据构造量测,利用卡尔曼滤波器对姿态误差进行估计并修正。仿真结果表明,该算法能有效完成载体机动检测,保证系统存在机动的情况下姿态精度满足应用要求。     

基于双轴转位机构的光纤陀螺标定方法研究

针对调制型捷联系统中光纤陀螺误差系数随时间变化的问题,提出一种利用双轴转位机构实现陀螺六位置静态标定方法.根据光纤陀螺仪误差模型,利用转位机构设计六位置标定路径,激励出陀螺仪标度因数、安装误差和零位,标定新方案避免了陀螺仪误差系数的耦合.分析了转位机构的转位误差对标定精度的影响,并利用调制型捷联系统导航实验对六位置标定方案进行原理性验证.结果表明,六位置标定方法所引起的系统定位精度优于传统标定方法.