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.

Robust UKF algorithm in SINS initial alignment

In the traditional unscented Kalman filter（UKF）,accuracy and robustness decline when uncertain disturbances exist in the practical system.To deal with the problem,a robust UKF algorithm based on an H-infinity norm is proposed.In Krein space,a robust element is added in the simplified UKF so as to improve the algorithm.The filtering gain is adjusted by the robust element and in this way the performance of the robustness of the filtering algorithm is promoted.In the initial alignment process of the large heading misalignment angle of the strapdown inertial navigation system（SINS）,comparative studies are conducted on the robust UKF and the simplified UKF.The simulation results illustrate that compared with the simplified UKF,the robust UKF is more accurate,and the estimation error of heading misalignment decreases from 16.9＇ to 4.3＇.In short,the robust UKF can reduce the sensitivity to the system disturbances resulting in better performance.

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.

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.

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.

Anti-interference self-alignment algorithm by attitude optimization estimation for SINS on a rocking base

The performance of a strapdown inertial navigation system(SINS)largely depends on the accuracy and rapidness of the initial alignment.A novel anti-interference self-alignment algorithm by attitude optimization estimation for SINS on a rocking base is presented in this paper.The algorithm transforms the initial alignment into the initial attitude determination problem by using infinite vector observations to remove the angular motions,the SINS alignment is heuristically established as an optimiza-tion problem of finding the minimum eigenvector.In order to further improve the alignment precision,an adaptive recursive weighted least squares(ARWLS)curve fitting algorithm is used to fit the translational motion interference-contaminated reference vectors according to their time domain characteristics.Simulation studies and experimental results favorably demonstrate its rapidness,accuracy and robustness.

Novel method of improving the alignment accuracy of SINS on revolving mounting base

In the process of initial alignment for a strapdown inertial navigation system （SINS） on a stationary base, the east gyro drift rate is an important factor affecting the alignment accuracy of the azimuth misalignment angle. When the Kalman filtering algorithm is adopted in initial alignment, it yields a constant error in the estimation of the azimuth misalignment angle because the east gyro drift rate cannot be estimated. To improve the alignment accuracy, a novel alignment method on revolving mounting base is proposed. The Kalman filtering algorithm of extending the measured values is studied. The theory of spectral condition number is utilized to analyze the degrees of observability of states. Simulation results show that the estimation accuracy of the azimuth misalignment angle is greatly improved through revolving mounting base, and the proposed method is efficient in initial alignment for a medium accurate SINS.

SINS in-Flight Alignment Using Quaternion Error Models

The widely used conventional linearized error models or perturbation models are not effective to represent the nonlinear characteristics of SINS error propagation with large attitude errors.Error equations in terms of quaternion error are derived,and extended Kalman filter techniques are used to solve the in-flight alignment problems.In the case of small attitude errors,the nonlinear models can be reduced to conventional phi-angle models.The simulation results show that the proposed error models may improve the performance of alignment.

Fast Iterative Closest Point-Simultaneous Localization and Mapping(ICP-SLAM)with Rough Alignment and Narrowing-Scale Nearby Searching

Two deficiencies in traditional iterative closest pointsimultaneous localization and mapping( ICP-SLAM) usually result in poor real-time performance. On one hand, relative position between current scan frame and global map cannot be previously known. As a result, ICP algorithm will take much amount of iterations to reach convergence. On the other hand,establishment of correspondence is done by global searching, which requires enormous computational time. To overcome the two problems,a fast ICP-SLAM with rough alignment and narrowing-scale nearby searching is proposed. As for the decrease of iterative times,rough alignment based on initial pose matrix is proposed. In detail,initial pose matrix is obtained by micro-electro-mechanical system( MEMS) magnetometer and global landmarks. Then rough alignment will be applied between current scan frame and global map at the beginning of ICP algorithm with initial pose matrix. As for accelerating the establishment of correspondence, narrowingscale nearby searching with dynamic threshold is proposed,where match-points are found within a progressively constrictive range.Compared to traditional ICP-SLAM,the experimental results show that the amount of iteration for ICP algorithm to reach convergence reduces to 92. 34% and ICP algorithm runtime reduces to 98. 86% on average. In addition,computational cost is kept in a stable level due to the eliminating of the accumulation of computational consumption. Moreover,great improvement can also been achieved in SLAM quality and robustness.

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。

基于变分贝叶斯的容积H_(∞)滤波在无人船载低成本MIMU对准的应用

无人船在恶劣海况下对其船载低成本微机械惯性测量单元(micro inertial measurement unit,MIMU)进行初始对准,其精度不仅会受到海浪摇摆、浪涌等未知复杂干扰的影响,还会受传感器噪声的影响。针对这一问题,提出基于变分贝叶斯的H_(∞)滤波算法(variational Bayesian cubature H_(∞)filter,VBCH),从而实现在系统噪声或状态方程不确定且系统噪声和量测噪声同时具有“时变”“厚尾”特性情况下的自适应鲁棒滤波。仿真实验表明,随着MIMU噪声的增大以及各项误差系数的不稳定性增强,系统噪声和状态方程逐渐变得不确定,基于卡尔曼滤波的自适应算法逐渐失效,而本文提出基于H_(∞)控制理论VBCH算法则能够继续保持一定的对准精度和算法鲁棒性:对无人船载低精度MIMU的天向对准精度达到1.5°,东向、北向对准精度能达到0.1°。满足无人船大失准角初始对准的要求,具有一定的应用价值。

基于稳健估计的稀疏网格积分滤波算法及其在捷联惯导系统对准中的应用

为有效解决捷联惯性导航系统对准过程中异常量测对状态估计精度的影响,该文提出一种基于稳健估计的稀疏网格积分滤波算法(RESGQF)。该文给出了精度为3级的稀疏网格采样点规则,引入稳健估计算法,构建针对对准系统各状态分量偏差的权重函数。基于稀疏网格积分滤波(SGQF)算法框架,利用权重矩阵实时对量测噪声进行更新,从而降低异常量测对系统状态的影响。通过模拟飞行器动机座空中对准过程,对比在复杂噪声环境下不同滤波方法的性能,证明所提算法提升了系统鲁棒性。

基于节点语义相似度的本体映射方法

针对本体映射特别是大尺度的异构本体映射由于语义异质性导致的映射精度和效率较低的问题,提出一种基于节点语义相似度的本体映射方法.首先,研究基于网络的本体解析和表示、本体自动分块、相似子本体快速识别、基于节点语义的子本体映射等关键技术;其次,以本体对齐评估倡议评估数据集中会议本体集进行实验,结果表明,该方法在性能上优于传统映射方法,在精度上高于基于片段的映射方法.

基于改进OBA的自适应反馈对准算法

针对无人机的空中对准问题,在有卫星导航的情况下,基于改进OBA(基于优化的对准)对准算法提出了一种统一粗对准和精对准过程的自适应反馈对准算法。在OBA算法后的每一步都进入自适应卡尔曼滤波器进行传感器误差估计和载体系姿态补偿,系统直接输出载体的实时姿态,缩短了对准时间。利用Sage-husa自适应估计器矫正系统的观测噪声协方差矩阵,再构造自适应因子来矫正系统状态量的协方差矩阵,通过KF(卡尔曼滤波器)来估计传感器的误差量,由反馈通道补偿给观测矢量,不断迭代得到姿态角对准结果。设计仿真实验,对卫星导航给出的速度与位置信息存在未知随机误差的情况进行模拟,发现改进后的OBA自适应反馈对准算法有效提高了原OBA算法的对准精度和应用场景,航向角误差由79.45°降低到了-0.032°。文中算法与EKF(扩展卡尔曼滤波)的对比实验,发现文中算法俯仰角与航向角收敛时间较EKF算法缩短了30 s左右,EKF算法收敛后,文中算法误差比EKF算法小2.203°。文中给出算法在量测噪声未知的大失准角初始对准中更有优势,能够应用于大机动飞行器的空中初始对准。

基于惯性传感器的免对准动作的人体上肢运动捕捉方法

基于惯性传感器的人体运动捕捉系统,通常在进行运动捕捉前需要执行特定的标准动作来完成对准,以获得IMU坐标系相对肢体坐标系的姿态。运动受限用户(如偏瘫患者)无法完成一些标准动作,而运动正常用户也不能保证均能正确执行标准动作,从而导致人体运动捕捉误差。为了克服现有方法的不足,研究了一种基于惯性传感器的免对准动作的人体上肢运动捕捉方法,该方法无需执行特定对准动作,佩戴IMU后即可直接进行运动捕捉。通过与“T-Pose”静态对准方案比较,验证了免对准动作方法的有效性,并且基于OpenGL的虚拟人体模型进行了上肢运动捕捉实验。实验表明,所提出的方法简便有效,在无须执行标准对准动作的情况下仍具有较好的人体运动捕捉效果,适合偏瘫患者等行动受限的用户使用。

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SUKF-KF混合滤波初始对准算法研究

针对初始对准UKF算法计算量大、运算效率低的问题,提出一种适用于SINS静基座大方位失准角初始对准的SUKF-KF混合滤波算法。将非线性误差模型分解为线性部分和非线性部分,线性部分采用KF滤波处理,由于非线性部分量测方程为线性,可对量测更新部分进行线性化处理,采用简化无迹卡尔曼滤波方法进行处理。仿真试验结果表明,在两种大方位失准角条件下,SUKF-KF算法相比UKF算法收敛速度分别提升14%和16%,收敛精度与UKF相当,SUKF-KF算法可以有效减少计算量、改进实时性。