A fast and accurate initial alignment method for strapdown inertial navigation system on stationary base

In this work,a fast and accurate stationary alignment method for strapdown inertial navigation system （SINS） is proposed. It has been demonstrated that the stationary alignment of SINS can be improved by employing the multiposition technique,but the alignment time of the azimuth error is relatively longer. Over here, the two-position alignment principle is presented. On the basis of this SINS error model, a fast estimation algorithm of the azimuth error for the initial alignment of SINS on stationary base is derived fully from the horizontal velocity outputs and the output rates, and the novel azimuth error estimation algorithm is used for the two-position alignment. Consequently, the speed and accuracy of the SINS＇ s initial alignment is enhanced greatly. The computer simulation results illustrate the efficiency of this alignment method.

Comparison of robust H_∞ filter and Kalman filter for initial alignment of inertial navigation system

There are many filtering methods that can be used for the initial alignment of an integrated inertial navigation system. This paper discussed the use of GPS, but focused on two kinds of filters for the initial alignment of an integrated strapdown inertial navigation system (SINS). One method is based on the Kalman filter (KF), and the other is based on the robust filter. Simulation results showed that the filter provides a quick transient response and a little more accurate estimate than KF, given substantial process noise or unknown noise statistics. So the robust filter is an effective and useful method for initial alignment of SINS. This research should make the use of SINS more popular, and is also a step for further research.

Application of H_∞ filtering in the initial alignment of strapdown inertial navigation system

In this paper , the principle of H∞ filtering is discussed and H_∞ filter is constructed, which is used in the initial alignment of the strapdown inertial navigation systems(SINS). The error model of SINS is derived. By utilizing constructed H∞ filter, the filtering calculation to that system has been conducted. The simulation results of the misalignment angle are given under the condition of unknown noises. The results show that the process of alignment with H∞ filter is much faster and with excellent robustness.

Initial Alignment Technique for SINS of Vehicles in the Moving State

An initial alignment technique for the strapdown inertial navigation system (SINS) of vehicles in the moving state is researched. By selecting an odometer as the system’s external sensor, the mathematical model for the alignment in the moving state is established and the observability of the system is analyzed. The results show that the SINS can successfully achieve the precision alignment in 10 min when the vehicle is moving toward the prearranged place after its staying for several seconds to perform the coarse alignment. The precision of alignment can also be improved in the moving state compared with that in the static state.

New celestial assisted INS initial alignment method for lunar explorer

In the future lunar exploration programs of China, soft landing, sampling and returning will be realized. For lunar explorers such as Rovers, Landers and Ascenders, the inertial navigation system （INS） will be used to obtain high-precision navigation information. INS propagates position, velocity and attitude by integration of sensed accelerations, so initial alignment is needed before INS can work properly. However, traditional ground-based initial alignment methods cannot work well on the lunar surface because of its low rotation rate （0.55°/h）. For solving this problem, a new autonomous INS initial alignment method assisted by celestial observations is proposed, which uses star observations to help INS estimate its attitude, gyroscopes drifts and accelerometer biases. Simulations show that this new method can not only speed up alignment, but also improve the alignment accuracy. Furthermore, the impact factors such as initial conditions, accuracy of INS sensors, and accuracy of star sensor on alignment accuracy are analyzed in details, which provide guidance for the engineering applications of this method. This method could be a promising and attractive solution for lunar explorer＇s initial alignment.

基于点线的视觉惯性导航系统初始化方法

视觉惯性导航系统在初始化阶段解算出系统初始位姿、惯性测量单元(IMU)零偏以及单目尺度因子,能极大程度上保证系统收敛。为解决视觉惯性导航系统在点特征缺失环境下初始化效果差甚至失败的问题,提出了一种基于点线的视觉惯性导航系统初始化方法。首先,通过几何方法在滑动窗口内构造了一个基于点线的结构,该结构相比于点结构具有更丰富的先验信息;其次,使用从运动中恢复结构(Sf M)的方法,恢复点线结构的运动状态;最后,以松组合的形式对齐结构的运动状态以及IMU预积分结果,求解出IMU零偏、重力加速度、速度和尺度因子等初始量。实验结果表明:在点特征缺失的公共数据集上,相较于基于点特征的初始化方法,所提方法的均方根定位误差减少6%,初始误差显著降低,同时具有更好的建图结果。

两种双矢量SINS粗对准方法大俯仰角适应性研究

大俯仰角下捷联惯导粗对准双矢量定姿中,不同矢量组合下,姿态角尤其是方位角对准精确度相差很大,必须慎重选择。对此,给出了2种最常用的粗对准矢量组合模式误差方程,对其主要误差影响因素进行分析。综合传感器误差,利用蒙特卡洛随机打靶方法对全姿态角下粗对准精确度进行仿真,并开展了80°俯仰角转台静态粗对准试验。转台试验中,在惯导陀螺误差约1°/h,加速度计误差约0.1mg情况下,第1种、第2种组合模式方位角粗对准最大误差值分别为20°、7.5°,第2种组合模式约为第1种的1/3;仿真测试中,在惯导陀螺误差约0.1°/h,加速度计误差约1mg,80°俯仰角下,第1种、第2种组合模式下方位角粗对准最大误差值分别为11°与2.2°,前者约为后者的5倍。仿真与试验结果表明,大俯仰角下矢量组合模式是影响惯导姿态角与姿态矩阵粗对准精确度的最主要因素。

卡尔曼滤波器异常自检及其在SINS初始对准中的应用

卡尔曼滤波器是惯性导航系统初始对准和组合导航问题中的常用工具。针对滤波时系统噪声和观测噪声异常导致的滤波精度下降甚至发散问题,从理论上分析了基于新息协方差匹配和马氏距离卡方检验的两种滤波器异常检验判据,结合判据在传统卡尔曼滤波、无迹卡尔曼滤波和容积卡尔曼滤波中的典型应用,设计了针对捷联式惯性导航系统初始对准问题的测试实验,实验结果表明两种判据对滤波器系统噪声和观测噪声异常都有一定的检验能力,但检验判据严格程度存在不同且判据存在拖尾效应,由此给出了在实践应用中的建议。

基于Kalman滤波的分段积分闭环回溯快速粗对准方法

快速启动能力是惯导系统的重要性能指标。为应对车辆与武器系统等在短时间内完成初始对准的需求,在基于Kalman滤波的闭环粗对准基础上提出了一种闭环回溯粗对准方法,并设计了一种分段积分矢量构建方法,使得算法能够在多次回溯的过程中构建更多不共线矢量,进一步减少粗对准所需的数据量并提升对准精度。经三轴转台及车载惯导30 s和50 s对准实验验证,本算法在短时间内的对准精度优于优化对准方法和基于Kalman滤波的闭环粗对准方法,能够满足载体进行快速初始对准的需求。

GNSS/SINS组合导航的动基座初始对准

针对传统的最优估计对准算法(OBA)无法应用于低成本INS中的问题,本文提出了一种结合扩展卡尔曼滤波的改进OBA算法,在GNSS的辅助下实现算法对姿态误差的估计,并使其能够应用于低成本SINS系统中。该算法首先重构参考向量和观测向量,以固定积分间隔抑制零偏的积累;然后通过建立陀螺仪零偏与姿态误差之间的联系,构建新的系统状态方程,并结合GNSS输出的位置与速度构建量测方程,同时对陀螺零偏和姿态误差进行估计,并将姿态误差反馈到多矢量的构建当中;最后在仿真试验与实际跑车试验中进行验证。试验结果表明,该算法与传统OBA算法相比,可以实现动基座快速对准,且可对陀螺零偏进行估计和失准角补偿,在载体处于长时间运动状态下提高了对准精度,不使用滑动窗口积分提高了收敛速度;与FIMA算法相比,3个姿态角精度分别提升了47%、47%、51%。

基于纯惯性导航系统的智慧铁路人员定位系统研究

针对GPS与IMU组合导航定位系统在不同应用场景下,存在GPS无线信号失锁和惯性传感器导航解算过程误差累积等问题,提出了一种纯惯性导航系统的智慧铁路人员定位方法。利用惯性传感器采集铁路巡检员的足部数据,并分析其运动特征;提出机器学习智能分类算法,检测行人在行走过程中的静止阶段和运动阶段。在初始状态下进行初始对准,以确定初始方位;在零速状态下,通过扩展卡尔曼滤波估计航向误差、角速度误差、速度误差,实现导航解算的姿态、速度和位置的零速修正。对比巡检员导航实验与GPS RTK差分定位实验发现,提出的基于纯惯性导航系统的智慧铁路人员定位系统可以准确跟踪巡检员的实时位置,并且平均误差为2.674%,最大误差不超过3.3%。

Rapid and robust initialization for monocular visual inertial navigation within multi-state Kalman filter

Sensor-fusion based navigation attracts significant attentions for its robustness and accuracy in various applications. To achieve a versatile and efficient state estimation both indoor and outdoor, this paper presents an improved monocular visual inertial navigation architecture within the Multi-State Constraint Kalman Filter （MSCKF）. In addition, to alleviate the initialization demands by appending enough stable poses in MSCKF, a rapid and robust Initialization MSCKF （I-MSCKF） navigation method is proposed in the paper. Based on the trifocal tensor and sigmapoint filter, the initialization of the integrated navigation can be accomplished within three consecutive visual frames. Thus, the proposed I-MSCKF method can improve the navigation performance when suffered from shocks at the initial stage. Moreover, the sigma-point filter is applied at initial stage to improve the accuracy for state estimation. The state vector generated at initial stage from the proposed method is consistent with MSCKF, and thus a seamless transition can be achieved between the initialization and the subsequent navigation in I-MSCKF. Finally, the experimental results show that the proposed I-MSCKF method can improve the robustness and accuracy for monocular visual inertial navigations.

On inertial navigation and attitude initialization in polar areas

Inertial navigation and attitude initialization in polar areas become a hot topic in recent years in the navigation community,as the widely-used navigation mechanization of the local level frame encounters the inherent singularity when the latitude approaches 90°.Great endeavors have been devoted to devising novel navigation mechanizations such as the grid or transversal frames.This paper highlights the fact that the common Earth-frame mechanization is sufficiently good to handle the singularity problem in polar areas.Simulation results are reported to demonstrate the singularity problem and the effectiveness of the Earth-frame mechanization.

抗线晃动干扰惯性系初始对准改进参数辨识方法

针对捷联惯导系统晃动基座惯性系初始对准中的参数辨识问题,提出了基于比力视位置拟合的抗线晃动干扰惯性系初始对准改进参数辨识方法。通过对惯导比力方程变换建立了比力双重积分视位置观测矢量模型,对惯导初始速度和位置变化两项参数进行参数辨识。在完成参数辨识之后,进行视位置观测矢量重构,重构算法从原理上消除了惯导初始速度和位置变化的影响,从而具备晃动基座初始对准收敛速度快和收敛后波动小的优势。进行了六自由度海浪晃动模拟验证,在1 m/s的初始线晃动情况下,采用所提方法1200 s初始对准方位精度约2.2’,较传统方法对准精度提高一个数量级。

基于观测量扩展的捷联惯导快速对准方法

为了研究惯性器件信息对捷联惯导系统(SINS)的影响,在静基座条件下,基于SINS简化误差模型,建立了采用速度误差、等效陀螺角速度误差和等效比力误差作为系统观测量的改进扩展观测方程,通过奇异值分解法(SVD)进行可观测性分析,引入等效陀螺角速度误差和等效比力误差的方法实现了系统全部状态的完全可观测性.仿真实验表明:与常规方法相比,该方法引入等效比力误差加速了水平失准角的收敛,引入等效陀螺角速度误差提高了方位失准角的收敛速度,同时状态的完全可观测性提升了初始对准精度.

DVL辅助的无人水下航行器回溯式无纬度运动对准方法

针对无人水下航行器需要精确的姿态信息而位置信息无法获得或无需提供的应用场景,提出一种多普勒测速仪(DVL)速度辅助的无纬度回溯运动对准方法。在粗对准阶段,通过对惯性系视速度矢量拟合粗略估计惯性导航系统(INS)的姿态矩阵和纬度信息,并完成惯性传感器数据的抽取和存储;在精对准阶段,重复利用粗对准阶段的存储数据完成INS/DVL卡尔曼滤波精对准。采用数据压缩存储和正向回溯导航技术,减小了存储空间和计算量,等效缩短了对准时间。船载实验表明,算法可在初始纬度未知条件下10min完成行进间对准,航向精度达0.085°,与纬度已知的传统对准方法精度相当。

一种基于相关特性的低精度IMU初始方位角对准方法研究

为降低陀螺制造成本,本文针对只需定向而对定位精度要求不高的捷联惯性导航系统,提出了一种两位置对准改进优化方法。该方法基于对准误差与水平X、Y轴陀螺随机噪声在东向投影的相关性,且与惯性导航设备所处的方位角有关的原理,通过在精对准时将惯性测量单元(IMU)转到0°和18 0°,降低Y、Z轴的陀螺精度对初始对准精度的影响,从而实现以较低精度陀螺完成精确初始对准的目的。最终进行了相关仿真试验和实物测试,结果表明,本文提出的方法在Y、Z轴陀螺精度降低50%时,仍能保持初始对准精度不变,进一步验证了本文所提方法的有效性。

基于GNSS的站内列车自主定位方法

列车精确自主定位是列车运行防护过程的一项核心关键技术。提出一种基于全球导航卫星系统同时考虑轨道占用状态转移的贝叶斯估计算法,将列车可能占用的区段放入备选集合,并对备选集合不断更新、评估、选择。在静态初始定位过程中,使用极大似然估计算法计算列车在区段上最可能的位置;在跟踪定位过程中,使用卡尔曼滤波算法将全球导航卫星系统和速度传感器融合实现精确定位。对列车实际行车数据分析的结果表明:在静态初始定位过程中,占用轨道识别正确率为100%;在跟踪定位过程中,占用轨道识别正确率为100%,平均误差为0.76 m。本方法满足列车运行防护的运用要求,对卫星导航定位在铁路中的应用具有借鉴意义。

基于正逆向滤波的四位置对准

为实现捷联惯导初始对准的快速性和高精度,提出一种基于正逆向滤波的四位置初始对准算法。与传统的先粗对准再精对准的两段式对准法不同,所提出的方案将整个对准过程分为3个阶段:粗对准、回溯对准、精对准。在第一阶段进行粗对准并采集存储数据;在第二阶段使用无迹卡尔曼滤波(Unscented Kalman filter,UKF)对系统状态进行回溯对准;在第三阶段采用KF进行精对准。整个过程中采样四位置数据。用所提方案进行初始对准,在保证快速对准的基础上,实现了高精度对准效果。仿真实验表明:该对准方案可以快速完成初始对准,并达到较高的对准精度,天向失准角误差在10″以内。

视觉惯性导航系统初始化方法综述

视觉惯性导航系统通过初始化,对尺度信息、重力向量、速度、惯性传感器偏差等一系列状态估计所需参数进行快速求解,以提升系统后续导航定位与环境感知的准确性。根据传感信息耦合方式,视觉惯性导航系统初始化方法可以分为三类:联合初始化、非联合初始化和半联合初始化。基于现有研究工作,从基础理论、发展与分类、现有方法、性能评估四个方面展开,对目前主流的初始化方法进行综述,并总结视觉惯性导航系统初始化领域未来的发展趋势,有利于对视觉惯性导航系统初始化方法形成总体性了解并把握其发展方向。