High accuracy navigation information estimation for inertial system using the multi-model EKF fusing adams explicit formula applied to underwater gliders

The underwater navigation system, mainly consisting of MEMS inertial sensors, is a key technology for the wide application of underwater gliders and plays an important role in achieving high accuracy navigation and positioning for a long time of period. However, the navigation errors will accumulate over time because of the inherent errors of inertial sensors, especially for MEMS grade IMU (Inertial Measurement Unit) generally used in gliders. The dead reckoning module is added to compensate the errors. In the complicated underwater environment, the performance of MEMS sensors is degraded sharply and the errors will become much larger. It is difficult to establish the accurate and fixed error model for the inertial sensor. Therefore, it is very hard to improve the accuracy of navigation information calculated by sensors. In order to solve the problem mentioned, the more suitable filter which integrates the multi-model method with an EKF approach can be designed according to different error models to give the optimal estimation for the state. The key parameters of error models can be used to determine the corresponding filter. The Adams explicit formula which has an advantage of high precision prediction is simultaneously fused into the above filter to achieve the much more improvement in attitudes estimation accuracy. The proposed algorithm has been proved through theory analyses and has been tested by both vehicle experiments and lake trials. Results show that the proposed method has better accuracy and effectiveness in terms of attitudes estimation compared with other methods mentioned in the paper for inertial navigation applied to underwater gliders.     

Initial orientation of inertial navigation system realized through nonlinear modeling and filtering

Alignment is the process whereby the orientation of the axes of an inertial navigation system is determined with respect to the reference system. In this paper, the initial alignment error equations of the strapdown inertial navigation system (SINS) with large initial azimuth error have been derived with inclusion of nonlinear characteristics. Simulations have been carried out to validate and corroborate the stationary alignment case employing a strapdown inertial measurement unit (SIMU). A performance comparison between the extended Kalman filter (EKF), the unscented Kalman filter (UKF) and the second-order divided difference filter (DDF2) demonstrate that the accuracy of attitude error estimation using the DDF2 is better than that of using the EKF or the UKF.     

Implementation of real-time positioning system using extended Kalman filter and artificial landmark on ceiling

Most localization algorithms use a range sensor or vision in a horizontal view, which usually imparts some disruption from a dynamic or static obstacle. By using landmarks on ceiling which the vehicle position were vertically measured, the disruption from horizontal view was reduced. We propose an indoor localization and navigation system based on an extended Kalman filter (EKF) and real-time vision system. A single upward facing digital camera was mounted on an autonomous vehicle as a vision sensor to recognize the landmarks. The landmarks consisted of multiple circles that were arranged in a defined pattern. Information on a landmark’s direction and its identity as a reference for an autonomous vehicle was produced by the circular arrangements. The pattern of the circles was detected using a robust image processing algorithm. To reduce the noise that came from uneven light, the process of noise reduction was separated into several regions of interest. The accumulative error caused by odometry sensors (i.e., encoders and a gyro) and the vehicle’s position were calculated and estimated, respectively, using the EKF algorithm. Both algorithms were tested on a vehicle in a real environment. The image processing method could precisely recognize the landmarks, and the EKF algorithm could accurately estimate the vehicle’s position. The experimental results confirmed that the proposed approaches are implementable.     

基于单星测量的星光惯性组合导航

为减小杂光影响,全天时星光定向仪一般采用小视场,同一时刻只能观测一颗恒星无法输出姿态信息.本文提出一种基于单星测量的星光惯性组合导航系统,首先根据惯导输出的姿态和位置信息,控制转台和星光定向仪摆镜保证星光定向仪对目标导航星的观测,之后根据惯导的误差模型建立系统状态方程,根据星光定向仪测量的导航星方向矢量建立测量方程,利...     

捷联惯导系统误差模型与仿真分析

为研究捷联惯导系统短时间导航精度,建立了导航误差数学模型,分析了惯性器件误差对系统导航精度的影响。应用捷联惯性导航原理,针对系统短时间导航的特点,简化载体在导航坐标系的导航方程;由惯性器件安装误差与陀螺仪等效零漂经过方向余弦矩阵变换建立载体姿态误差方程;结合导航方程、姿态误差方程与惯性器件误差推导出载体速度误差与位置误差数学模型。在此基础上,建立了误差状态空间方程与误差模型框图。在Matlab/Simulink环境下建立了误差数学模型计算模块,用捷联惯导算法与误差模型共同解算地面150秒导航试验数据,结果表明：导航系X轴的相对系统误差小于20%,Y轴、Z轴的相对系统误差小于5%,验证了误差数学模型的正确性。此外,分析了加速度计精度的变化对短时间工作的捷联惯导系统导航误差产生基本的影响。     

Outdoor position estimation based on a combination system of GPS-INS by using UPF

This paper proposes a technique that global positioning system(GPS)combines inertial navigation system(INS)by using unscented particle filter(UPF)to estimate the exact outdoor position.This system can make up for the weak point on position estimation by the merits of GPS and INS.In general,extended Kalman filter(EKF)has been widely used in order to combine GPS with INS.However,UPF can get the position more accurately and correctly than EKF when it is applied to real-system included non-linear,irregular distribution errors.In this paper,the accuracy of UPF is proved through the simulation experiment,using the virtual-data needed for the test.     

Improving ultrasonic-based seamless navigation for indoor mobile robots utilizing EKF and LS-SVM

The ultrasonic positioning system is able to provide centimeter-level location information. However, the signal of the system is easy to be disturbed and the outages of the positioning system appear. Inertial measuring units (IMUs) is a self-contained device and can provide long-term navigation information independently, but it has the drawback of error drift. In order to obtain accurate and continuous location information indoors for indoor mobile robots, this work proposed a seamless integrated navigation utilizing extended Kalman filter (EKF) and Least Squares Support Vector Machine (LS-SVM). In this mode, the EKF estimates the position and the velocity of the robot while the signals of ultrasonic positioning system are available. Meanwhile, the compensation model is trained by LS-SVM with corresponding filter states. Once the signals of ultrasonic positioning system are outages, the model is able to correct inertial navigation system (INS) solution as filter does. A prototype of the system has been worked in a real scenario. The results show that the performance of EKF is robust, and the prediction of LS-SVM is able to work as EKF does during the outages.     

交互多模型在车辆组合定位系统中的应用（英文）

为解决扩展卡尔曼滤波器(extended Kalman filter,EKF)在车辆组合定位系统中因车辆加减速、转弯(以下简称机动)而存在的精度低、稳定性差等问题,设计了一种将交互多模型(interacting multiple model,IMM)算法与非线性卡尔曼滤波器相融合的自适应滤波算法。该算法使用三种状态空间模型来描述车辆的运动模式,采用多个非线性滤波器对每个模型并行滤波,通过模型匹配似然函数对滤波结果进行加权融合,最终得到系统的定位信息。该方法具备非线性系统滤波器优点,克服了单一模型滤波算法对机动目标定位效果差的缺点。利用该方法和EKF算法分别对GPS/INS/DR车辆组合定位系统中进行了仿真实验,结果表明,该算法的滤波定位精度明显优于目前组合定位系统中所用的EKF滤波器,大幅提高了组合定位系统的稳定性和定位精度。     

MEMS-based rotary strapdown inertial navigation system

As the inertial navigation completely depends on the sensed acceleration and rotation rate by IMU, the sensor errors accumulate and eventually lead to diverged inertial solutions. Therefore the compensation of the inertial sensor errors is an effective approach to improve the SINS navigation performance. The rotation error modulation in rotary SINS, which has been extensively used for the filter-optical IMU in the past, is one of the techniques to compensate or mitigate the inertial sensor errors and eventually improve the system navigation performance. The rotary SINS is an inertial navigator in which the IMU is installed on the rotational platform and rotated following the pre-designed rotation configuration, and the rotation error modulation is the technique that compensates the navigation errors caused by inertial sensor bias in a complete rotation cycle by rotating IMU. Given the auto-compensation of inertial sensor bias in rotation error modulation, the objective of this paper to develop a MEMS-based rotary SINS, in which the significant sensor bias is automatically compensated by rotating the IMU, to offer the comparable navigation performance to tactical-grade IMU. Simulation results indicate that, compared with the conventional method, the proposed approach attenuates the navigation errors and improve the calibration accuracy based on the reciprocating rotation scheme can be used to automatically improve the observability.     

利用火星卫星光学测量实现火星探测器自主导航

以火星探测为例,提出了通过对火星卫星进行光学测量实现火星探测器自主导航的方法。该方法在火星探测器上搭载光学相机,在飞向火星过程中对火星天然卫星（Phobos,火卫一;Deimos,火卫二）拍摄带有恒星背景的图像;通过恒星位置确定精确的惯性指向,利用得到的光学观测数据完成对火星探测器的自主导航。分别给出了基于扩展卡尔曼滤波（EKF）和无迹卡尔曼滤波（UKF）进行自主导航的方法和仿真计算结果。数据显示：EKF和UKF得到的结果基本一致,说明EKF在线性化过程中损失精度并不多。在巡航段后半程,与火星距离越近,导航精度越高。距离火星（1~5）×10⁷ km时,取数据间隔为1 min,如果测量精度为0.1",导航精度可达10~100 km量级,速度精度为0.01 m/s量级;如果测量精度为1",导航精度也相应要低一个量级。另外,单独使用火卫二的导航精度要高于单独使用火卫一,联合使用火卫一和火卫二的精度最高。仿真计算结果表明,利用火星卫星光学测量的火星探测器自主导航,可满足火星探测器高精度导航的要求。     

引入滑模观测器的GPS/INS组合导航滤波方法

由低成本器件组成的卫星/惯性(GPS/INS)组合导航系统中,存在较大的非线性与不确定性,为改善这一问题,本文提出一种引入滑模观测器(SMO)的滤波方法。首先,该方法建立了组合导航系统模型,介绍了扩展卡尔曼滤波(EKF)计算过程并分析存在的不足。然后,介绍了滑模观测器的基本原理,根据系统构建观测器。最后,说明了引入滑模观测器的EKF组合导航算法实现流程,滑模观测器将模型误差、状态估计以及均值方差融入EKF算法,修正系统输出。通过轨迹仿真实验与车载实验验证了所提方法优于传统EKF算法,具有更高的滤波精度。在车载实验中,卫星信号失锁15 s情况下,与EKF方法相比,所提方法的东向位置误差降低了53%,北向位置误差降低了37%,证明该方法能够有效抑制GPS/INS组合导航误差发散,为以后工程实践提供一定的参考价值。     

三视场红外地球敏感器导航算法数值分析及实验研究

红外地球敏感器广泛运用空间飞行器,尤其是在地球轨道附近。目前最新的静态地球敏感器使用焦平面探测器来覆盖整个地球红外辐射圆。若面向飞行在70km-100km处的临近空间飞行器,他们就需要有很大的视场(>120°)且相对分辨率很低。本文针对临近空间飞行器的天文导航运用,采用线阵CCD作为探测器,设计了一个三视场红外地球敏感器。他们按照二维角度均匀分布以确保每个光学头能够均匀的指向地球红外辐射圆上。每个CCD覆盖20°以便能够精确地分辨地球辐射和空间的边界点。综合考虑地球辐射模型和地球扁率等因素,利用地球敏感器观测的向量可以获得地心矢量和飞行高度。以70-100km飞行高度为示例,建立相应的数学模型,同时提出了相应的姿态和高度确定算法并进行了仿真。综上述分析,研制了一套三视场地球敏感器原理样机并进行相关测试。仿真和实验结果表明：高度测量精度优于200m,姿态测量精度优于0.002°。     

运载火箭上面级惯性与天文组合导航系统设计

针对运载火箭上面级惯性导航随时间累积而误差增大以至不能满足长时间工作要求的问题,对采用星敏感器和地球敏感器修正惯性导航误差的方案进行了研究。首先,导出了上面级常用坐标系定义和姿态转换矩阵。然后,根据惯性导航的误差传播特性、星敏感器测量方程和地球敏感器的模拟测量方程,给出了组合导航的状态方程和观测方程。最后,设计了基于Matlab/dSpace仿真平台的星敏感器在导航回路中的半物理仿真实验。实验结果表明,组合导航使惯性导航位置误差矢量和从1.1719×104m减小到1.0367×103m,速度误差矢量和从11.2827m/s减小到3.6626m/s,姿态误差从0.1°减小到5′,说明了该组合导航方案能够有效修正惯性导航时间累积误差,半实物仿真实验验证了惯性/天文组合导航方案的可行性与正确性。     

无人机磁惯导系统中航向校正的双内积算法

针对无人机磁惯导系统中广泛采用的三轴磁强计,建立航向角误差模型,分析出航向角的非对准误差等效为常值误差加半圆罗差,提出了一种基于双内积的航向误差校正方法,即利用地磁场矢量与自身内积得到的模值为定值以及地磁矢量与重力矢量二者的内积为常数原理进行航向角解算补偿。该方法能克服基于矢量模值不变校正方法无法补偿非对准误差的缺陷,可实现三轴磁强计的完全校正。数值仿真及实验结果显示,该方法校正效果优于标量校正法、点积不变法以及两步法,能有效降低磁场矢量的模值误差和无人机航向角误差,且对磁惯导系统中的传感器噪声有较好的鲁棒性。     

A derivative UKF for tightly coupled INS/GPS integrated navigation

The tightly coupled INS/GPS integration introduces nonlinearity to the measurement equation of the Kalman filter due to the use of raw GPS pseudorange measurements. The extended Kalman filter (EKF) is a typical method to address the nonlinearity by linearizing the pseudorange measurements. However, the linearization may cause large modeling error or even degraded navigation solution. To solve this problem, this paper constructs a nonlinear measurement equation by including the second-order term in the Taylor series of the pseudorange measurements. Nevertheless, when using the unscented Kalman filter (UKF) to the INS/GPS integration for navigation estimation, it causes a great amount of redundant computation in the prediction process due to the linear feature of system state equation, especially for the case with system state vector in much higher dimension than measurement vector. To overcome this drawback in computational burden, this paper further develops a derivative UKF based on the constructed nonlinear measurement equation. The derivative UKF adopts the concise form of the original Kalman filter (KF) to the prediction process and employs the unscented transformation technique to the update process. Theoretical analysis and simulation results demonstrate that the derivative UKF can achieve higher accuracy with a much smaller computational cost in comparison with the traditional UKF.     

The canonical stewart platform as a six DOF pose sensor for automotive applications

The growing demand for prolonged fatigue life of automotive parts and components requires elaboration of their motion in Cartesian space having six degrees of freedom (DOF). Recently, the canonical Steward platform, consisting of six displacement sensors mounted on two parallel platforms, was introduced to comply with this request. In order to apply this pose sensor to automotive applications, the following two important matters are investigated in this study. First, update Jacobian is proposed as a faster and more stable numerical method to solve the forward kinematic problem without any iteration process. Second, the attachment position and initial configuration of the Stewart platform must be adjustable to avoid the interference with other components due to space constraints under the hood of automotive vehicle. In this case, however, the Jacobian matrix which converts six displacement components into a six DOF pose vector is prone to be ill-conditioned so that the converting accuracy becomes worse. The L₁-norm of each row in the Jacobian matrix quantifies how much the error would be provoked according to the given kinematic geometry. Hence, it can be used here as a reliable error indicator. Furthermore, several numerical examples are discussed to demonstrate what to consider when designing a six DOF pose sensor for automotive applications.     

基于卷积神经网络与扩展卡尔曼滤波的 单目视觉惯性里程计

针对单目相机采集室外图像易受环境光照影响、尺度存在不确定性的缺点,以及利用神经网络进行位姿估计不准确的问题,提出一种基于卷积神经网络(CNN)与扩展卡尔曼滤波(EKF)的单目视觉惯性里程计。采用神经网络取代传统里程计中基于几何约束的视觉前端,将单目相机输出的估计值作为测量更新,并通过神经网络优化EKF的误差协方差。利用EKF融合CNN输出的单目相机位姿和惯性测量单元(IMU)数据,优化CNN的位姿估计,补偿相机尺度信息与IMU累计误差,实现无人系统运动位姿的更新和估计。相比于使用单目图像的深度学习算法Depth-VO-Feat,所提算法融合单目图像和IMU数据进行位姿估计,KITTI数据集中09序列的平动、转动误差分别减少45.4%、47.8%,10序列的平动、转动误差分别减少68.1%、43.4%。实验结果表明所提算法能进行更准确的位姿估计,验证了算法的准确性和可行性。     

Real-time accurate odometer velocity estimation aided by accelerometers

In strap-down gyro-compass in-motion alignment, the alignment accuracy depends not only on the quality of the gyroscopes and accelerometers, but also on the accuracy of the velocity provided by the aiding sensors such as odometers. To improve the accuracy of the in-motion alignment, real-time accurate odometer velocity estimation is required. In this paper, the effect of the noise of the odometer velocity on strap-down gyro-compass in-motion alignment accuracy is presented, based on the strap-down gyro-compass algorithm. A velocity tracking model is designed as the state model, to describe the relationship between and among the vehicle’s velocity, acceleration and jerk in the vehicle frame. Based on the velocity equation applied to strap-down navigation system mechanizations, the vehicle’s acceleration in the vehicle frame can be obtained from the specific forces measured by accelerometers. With the observations including the vehicle’s acceleration in the vehicle frame and the vehicle’s velocity in the vehicle frame obtained from the odometer using the first order difference algorithm, real-time velocity estimates are produced by a Kalman filter. The field test results show that the proposed method can successfully improve the accuracy of the odometer velocity. The comparison with the traditional method highlights the superior performance of the proposed method.     

基于原子体系的量子惯性传感器研究现状

惯性传感器的性能直接决定了惯性导航系统的精度。基于原子体系的量子惯性传感器有望在更小体积和更低成本下达到传统惯性传感器的性能,且理论上可以获得比现有技术更高的测量灵敏度和长期稳定性。近些年随着量子精密测量领域的快速发展,量子惯性传感器的实用化和工程化方面研究进展显著,未来通过替代传统加速度计和陀螺仪,有可能形成高度集成、低功耗和低漂移的量子惯性导航系统。文章简要介绍了基于原子体系的量子惯性传感器的基本原理,总结了以原子干涉陀螺仪、原子自旋陀螺仪、原子干涉加速度计、原子干涉重力仪和重力梯度仪为主的量子惯性传感器研究现状,并对有待解决的关键技术问题进行了梳理和分析,可为量子惯性传感器的发展提供参考。     

基于SINS/DR组合导航和地图匹配技术的地铁定位方法研究

随着轨道交通的发展,地铁在人们日常生活中扮演着越来越重要的角色,地铁的定位技术也是移动闭塞实现的关键.本文针对地铁无法使用GPS且地面设备复杂,成本高的缺点,提出了一种基于惯性测量单元(IMU)和速度传感器辅以轨道电子地图的自主定位方法,在抑制捷联惯导系统(SINS)的误差发散的同时,减少了航迹推算(DR)由于姿态角误差造成的累计误差.根据铁路线路的特殊性,提出了运用最小二乘进行地图匹配的方法校正组合定位带来的误差,极大提高了定位的精度,同时减少了对轨旁设备的依赖性,降低了造价和维护成本.