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.

An improved computation scheme of strapdown inertial navigation system using rotation technique

To improve the accuracy of strapdown inertial navigation system(SINS) for long term applications,the rotation technique is employed to modulate the errors of the inertial sensors into periodically varied signals,and,as a result,to suppress the divergence of SINS errors.However,the errors of rotation platform will be introduced into SINS and might affect the final navigation accuracy.Considering the disadvantages of the conventional navigation computation scheme,an improved computation scheme of the SINS using rotation technique is proposed which can reduce the effects of the rotation platform errors.And,the error characteristics of the SINS with this navigation computation scheme are analyzed.Theoretical analysis,simulations and real test results show that the proposed navigation computation scheme outperforms the conventional navigation computation scheme,meanwhile reduces the requirement to the measurement accuracy of rotation angles.

Fast Compass Alignment for Strapdown Inertial Navigation System

Initial alignment is the precondition for strapdown inertial navigation system(SINS)to navigate.Its two important indexes are accuracy and rapidity,the accuracy of the initial alignment is directly related to the working accuracy of SINS,but in self-alignment,the two indexes are often contradictory.In view of the limitations of conventional data processing algorithms,a novel method of compass alignment based on stored data and repeated navigation calculation for SINS is proposed.By means of data storage,the same data is used in different stages of the initial alignment,which is beneficial to shorten the initial alignment time and improve the alignment accuracy.In order to verify the correctness of the compass algorithm based on stored data and repeated navigation calculation,the simulation experiment was done.In summary,when the computer performance is sufficiently high,the compass alignment method based on the stored data and the forward and reverse navigation calculation can effectively improve the alignment speed and improve the alignment accuracy.

SINS/CNS/GPS integrated navigation algorithm based on UKF

A new nonlinear algorithm is proposed for strapdown inertial navigation system （SINS）/celestial navigation system （CNS）/global positioning system （GPS） integrated navigation systems. The algorithm employs a nonlinear system error model which can be modified by unscented Kalman filter （UKF） to give predictions of local filters. And these predictions can be fused by the federated Kalman filter. In the system error model, the rotation vector is introduced to denote vehicle＇s attitude and has less variables than the quaternion. Also, the UKF method is simplified to estimate the system error model, which can both lead to less calculation and reduce algorithm implement time. In the information fusion section, a modified federated Kalman filter is proposed to solve the singular covariance problem. Specifically, the new algorithm is applied to maneuvering vehicles, and simulation results show that this algorithm is more accurate than the linear integrated navigation algorithm.

Method of Improving the Navigation Accuracy of SINS by Continuous Rotation

A method of improving the navigation accuracy of strapdown inertial navigation system (SINS) is studied. The particular technique discussed involves the continuous rotation of gyros and accelerometers cluster about the vertical axis of the vehicle. Then the errors of these sensors will have periodic variation corresponding to components along the body frame. Under this condition, the modulated sensor errors produce reduced system errors. Theoretical analysis based on a new coordinate system defined as sensing frame and test results are presented, and they indicate the method attenuates the navigation errors brought by the gyros' random constant drift and the accelerometer's bias and their white noise compared to the conventional method.

A tightly coupled rotational SINS/CNS integrated navigation method for aircraft

Strapdown inertial navigation system(SINS)/celestial navigation system(CNS)integrated navigation is widely used to achieve long-time and high-precision autonomous navigation for aircraft.In general,SINS/CNS integrated navigation can be divided into two integrated modes:loosely coupled integrated navigation and tightly coupled integrated navigation.Because the loosely coupled SINS/CNS integrated system is only available in the condition of at least three stars,the latter one is becoming a research hotspot.One major challenge of SINS/CNS integrated navigation is obtaining a high-precision horizon reference.To solve this problem,an innovative tightly coupled rotational SINS/CNS integrated navigation method is proposed.In this method,the rotational SINS error equation in the navigation frame is used as the state model,and the starlight vector and star altitude are used as measurements.Semi-physical simulations are conducted to test the performance of this integrated method.Results show that this tightly coupled rotational SINS/CNS method has the best navigation accuracy compared with SINS,rotational SINS,and traditional tightly coupled SINS/CNS integrated navigation method.

Time-asynchrony identification between inertial sensors in SIMU

Traditional strapdown inertial navigation system （SINS） algorithm studies are based on ideal measurements from gy- ros and accelerometers, while in the actual strapdown inertial measurement unit （SIMU）, time-asynchrony between each iner- tial sensor is inevitable. Testing principles and methods for time- asynchrony parameter identification are studied. Under the single- axis swaying environment, the relationships between the SINS platform drift rate and the gyro time-asynchrony are derived using the SINS attitude error equation. It is found that the gyro time- asynchrony error can be considered as a kind of pseudo-coning motion error caused by data processing. After gyro testing and synchronization, the single-axis tumble test method is introduced for the testing of each accelerometer time-asynchrony with respect to the ideal gyro triad. Accelerometer time-asynchrony parame- ter identification models are established using SINS specific force equation. Finally, all of the relative time-asynchrony parameters between inertial sensors are well identified by using fiber optic gyro SIMU as experimental verification.

GNSS/SINS/视觉导航鲁棒算法

全球导航卫星系统(Global Navigation Satellite System, GNSS)、捷联惯性导航系统(Strapdown Inertial Navigation System, SINS)和视觉传感器优势互补,3者信息融合可获得高精度、无漂移的导航定位信息.针对GNSS/SINS/视觉融合导航易受运动速度、光照变化、遮挡等影响导致定位精度和鲁棒性降低问题,本文在图优化框架的代价函数中加入SoftLOne鲁棒核函数,设置量测值粗差检验程序,降低离群点带来的负面影响.进一步,对量测值计算残差进行卡方检验,对超限残差降权处理,提高系统精度和鲁棒性.实验结果表明,本文算法较不施加鲁棒核函数、不采用异常值剔除策略和卡方检验的传统算法,以及加入其他鲁棒核函数的算法精度更高、鲁棒性更好,能够较大程度提升GNSS/SINS/视觉导航定位精度和鲁棒性,在大尺度环境下,未出现较大漂移误差,绝对位姿均方根误差0.735 m,绝对位姿误差标准差0.336 m.

捷联惯导系统IMU误差特性分析

为了优化捷联惯导系统惯性测量单元性能,首先,从惯性导航系统工作原理出发,细致梳理捷联惯导误差模型,对惯性测量单元常见误差源分类,重点分析主流民航客机机型捷联惯导惯性测量单元的误差特性;然后,对由5个直线段和4个转弯组成的矩形起落航线,在无误差的理想模式和设置导航级捷联惯导惯性测量单元误差参数模式,分别进行轨迹仿真;最后,在飞行轨迹的姿态、速度、位置误差三维方向对比分析。结果表明,IMU误差诱使飞行轨迹随时间有偏离趋势,高度误差波动始终非常平稳,水平方向误差在一定时间后大幅度发散,表现出惯性导航性能的固有缺陷,即误差随时间的推移逐渐累积,会在一段平稳时间后呈发散趋势。因此在实际使用惯导中可以使用特殊的算法和硬件平台以延长误差发散之前的时间,也可以通过与GPS等数据进行组合导航的方式抑制其发散趋势。研究结果为进一步进行组合导航算法优化奠定基础。

机载捷联惯导系统振动性能提升研究

针对振动条件下,惯性测量组件产生耦合角振动,进而引起圆锥误差导致惯性导航精度降低的问题,提出了一种考虑减振器刚度不一致性与各向异性情况下,惯性测量组件振动耦合角运动的分析方法。从力学分析出发,推导了惯导系统空间六自由度矩阵振动微分方程;建立了惯导系统振动方程的数值解计算模型。其次,基于工程实际,使用数值仿真软件分析减振器批次刚度一致性和刚度各向异性的影响;并通过有限元仿真分析对比验证了减振器刚度波动导致的偏心距引起耦合角速度计算结果。最后分析了减振系统不同谐振频率对角振动幅值的影响机理。

DVL/SINS松紧组合的等价性分析及改进的虚拟波束辅助紧组合算法

系统地研究了不同波束可用情况下的多普勒计程仪(Doppler velocity logger,DVL)/捷联惯性导航系统(strapdown inertial navigation system,SINS)紧组合导航算法。证明了3个或4个波束可用情况下,DVL/SINS波束域紧组合的定位精度与三维速度松组合系统具有等价性,并给出了松组合中量测协方差阵的确定方法。针对紧组合系统定位精度随可用波束减少而降低的问题,考虑DVL的误差参数,基于载体运动约束和压力深度计提出一种改进的虚拟波束辅助紧组合算法。实验结果表明,3个或4个波束可用时的松组合与紧组合的定位精度等价;改进的虚拟波束辅助紧组合算法可以准确跟踪故障的波束速度,有效提升了不同波束可用情况下紧组合的定位精度。

基于SINS/OD失准角观测的垂线偏差测量方法

组合导航系统的姿态失准角与垂线偏差(deviation of vertical,DOV)存在对应关系,通过对姿态失准角的观测是实现DOV测量的一种有效手段。本文基于车载捷联惯性导航系统/里程计(strapdown inertial navigation system/odometer,SINS/OD)组合导航姿态失准角观测及失准角误差的递推提出一种DOV测量方法。首先,从组合导航速度误差方程出发,推导出DOV与姿态失准角之间存在复杂的耦合关系。然后,通过沿路径迭代递推的分步计算方法,由导航过程中的姿态失准角直接获取沿线的DOV。最后,仿真结果验证了该算法的有效性,且表明测量精度与行驶路径的长度有关。实验表明,行驶里程35 km的机动路径上DOV误差均值小于0.3″,终点处单点测量误差小于0.7″。

联合神经网络和对角加载的GNSS中断定位算法

GNSS/INS组合导航系统可以为移动载体提供长时间、高精度的导航信息,然而当载体处于恶劣环境中,无法获得滤波量测向量,导致导航定位结果迅速发散.为应对这一问题,越来越多的学者利用人工神经网络辅助组合导航系统直接进行信息融合.但惯性导航系统(inertial navigation system,INS)本身特性使得上一时间训练好的网络模型存在误差,中断时刻INS误差仍不断累积,因此提出了一种GNSS中断时的智能定位算法.该算法利用反向传播(back propagation,BP)神经网络训练得到滤波量测向量,再通过对角加载重构量测噪声协方差矩阵,对卡尔曼滤波(Kalman filter,KF)进行更新.所提方法减弱了神经网络训练误差对组合导航算法的影响,从而可以在GNSS信号长时间中断情况下,导航系统仍拥有较为可靠的导航性能.

基于分布式信息滤波的集群导弹协同定位方法

针对领从式集群导弹中的从弹定位问题,考虑领弹动态地切入切出导航网络情况,提出了一种基于捷联惯性导航系统(INS)与到达时间(TOA)测距系统相融合的领从类分布式协同导航方法。该方法综合考虑了导航参数与相关传感器误差模型,基于INS误差系统建立线性状态空间模型,基于弹间数据链TOA测距系统建立非线性伪距量测模型,并基于扩展信息滤波器实现动态信息的分布式融合,以适应量测信息节点数目动态变化引起的协同导航网络结构变化。将其应用于集群导弹协同导航系统中,仿真结果表明所提算法可以快速且高精度地完成从弹导航参数的估计,可以有效消除领弹节点变化所带来的负担。该方法可以作为低成本从弹导航系统的补充手段。

Evaluation and optimization of strapdown velocity numerical integration algorithms for SINS in spinning ballistic missiles

The error of the conventional velocity numerical integration algorithm was evaluated through the Taylor series expansion. It is revealed that neglecting the second- and higher-order terms of attitude increments will lead to the velocity numerical integration error, which is proportional to the triple cross product of the angular rate and specific force. A selection criterion for the velocity numerical integration algorithm was established for strapdown inertial navigation system (SINS) in spinning missiles. The spin angular rate with large amplitude will cause the accuracy of the conventional velocity numerical integration algorithm in SINS to decrease dramatically when the ballistic missile is spinning fast. Therefore, with the second- and higher-order terms of attitude increments considered, based on the rotation vector and the velocity translation vector, the velocity numerical integration algorithm was optimized for SINS in spinning ballistic missiles. The superiority of the optimized algorithm over the conventional one was analytically derived and validated by the simulation. The optimized algorithm turns out to be a better choice for SINS in spinning ballistic missiles and other high-precision navigation systems and high-maneuver applications.

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。

复杂城市环境下的全球导航卫星系统/捷联惯性导航系统组合导航算法

针对复杂城市环境下,全球导航卫星系统(GNSS)信号出现频繁短暂失锁或拒止时,对GNSS/捷联惯性导航系统(SINS)组合导航系统的导航精度和鲁棒性影响较大问题,该文提出一种改进的因子图滤波方法。首先使用GNSS接收机内部参数构建信号误差鉴别函数,能实时估计出信号受多径干扰、遮挡等情况下的信号测量性能;同时利用载体运动约束条件构造零速修正因子,对GNSS拒止情况下的系统状态进行更新,避免系统导航性能极速下降。实验结果表明,改进的因子图方法相比经典因子图方法,在城市复杂环境下能提高定位精度63.50%和测速精度42.26%,同时也具有更低的存储量和计算复杂度,特别适用于城市车辆辅助驾驶导航设备中,对导航精度、硬件资源和实时性约束强的场景。

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.

SINS/GNSS自适应SRCKF直接式组合导航方法

针对动基座发射条件以及高动态、强干扰的运动环境下飞行器组合导航系统由于外界干扰导致滤波精度下降和鲁棒性差的问题,研究了一种发射惯性系下SINS/GNSS自适应SRCKF直接式组合导航方法。首先基于发射惯性坐标系构建了SINS/GNSS直接式组合导航模型,然后采用残差卡方检验法对量测信息失准情况进行判别,最后将衰减因子引入SRCKF算法中,自适应的在线调整滤波器的观测误差协方差阵以提升滤波精度。仿真结果表明:在量测噪声变化的情况下,相比于常规SRCKF组合导航算法,自适应SRCKF直接式组合导航算法位置、速度精确明显提升,可有效提升飞行器组合导航系统精度和鲁棒性。

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。