Error Analysis and Compensation of Strapdown Inertial Navigation System

Based on error analysis, the influence of error sources on strapdown inertial navigation systems is discussed. And the maximum permissible component tolerances are established. In order to achieve the desired accuracy (defined by circular error probability), the types of appropriate sensors are chosen. The inertial measurement unit (IMU) is composed of those sensors. It is necessary to calibrate the sensors to obtain their error model coefficients of IMU. After calibration tests, the accuracy is calculated by uniform design method and it is proved that the accuracy of IMU is satisfied for the desired goal.

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.

DESIGN AND REALIZATION OF SINS/TWO-ANTENNA GPS INTEGRATED NAVIGATION SYSTEM

The strapdown inertial navigation system （SINS）/two-antenna GPS integrated navigation system is discussed. Corresponding error and the measurement models are built, especially the double differenced GPS carrier phase model. The extended Kalman filtering is proposed. And the hardware composition and connection are designed to simulate the SINS/two-antenna GPS integrated navigation system. Results show that the performances of the system, the precision of the navigation and the positioning, the reliability and the practicability are im proved.

Application of unscented Kalman filter to novel terrain passive integrated navigation system

To improve the navigation accuracy of an autonomous underwater vehicle （AUV）, a novel terrain passive integrated navigation system （TPINS） is presented. According to the characteristics of the underwater environment and AUV navigation requirements of low cost and high accuracy, a novel TPINS is designed with a configuration of the strapdown inertial navigation system （SINS）, the terrain reference navigation system （TRNS）, the Doppler velocity sonar （DVS）, the magnetic compass and the navigation computer utilizing the unscented Kalman filter （UKF） to fuse the navigation information from various navigation sensors. Linear filter equations for the extended Kalman filter （EKF）, nonlinear filter equations for the UKF and measurement equations of navigation sensors are addressed. It is indicated from the comparable simulation experiments of the EKF and the UKF that AUV navigation precision is improved substantially with the proposed sensors and the UKF when compared to the EKF. The TPINS designed with the proposed sensors and the UKF is effective in reducing AUV navigation position errors and improving the stability and precision of the AUV underwater integrated navigation.

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.

Subway location method based on SINS/DR integrated navigation and map-matching technology

With the development of rail transit,subway is playing an increasingly important role in peoples daily life.The positioning technology of subway is the key of communication based on train control system(CBTC).Considering that the global positioning system(GPS)cant be utilized in the subway and the ground equipment is complex and expensive,a self-positioning method based on inertial measurement unit(IMU)and speed sensor is put forward,and the track electronic map is used to reduce the error.This method can suppress the error divergence of Strapdown inertial navigation system(SINS)and reduce the cumulative error of dead reckoning(DR)due to attitude error.In accordance with the particularity of railway lines,using the least squares method to match the line and revise the error caused by the navigation,can greatly improve the positioning accuracy and reduce the dependency on the ground equipment,and the costs of construction and maintenance can be lowered.

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.

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。

一种基于深度学习辅助的SINS/DVL组合导航方法

水下机器人(autonomous underwater vehicle,AUV)导航定位精度一定上程度影响了AUV的工作效率,由于GNSS无法在水下使用,以捷联惯导系统/多普勒计程仪(SINS/DVL)的组合导航系统受到众多青睐。DVL在部分情况下会失效,若直接将DVL隔离,系统将变为纯惯性导航系统,严重影响导航定位的精度。为了应对DVL在部分波束缺失的情况,提出了一种DLinear-informer辅助组合导航算法。通过DLinear对原始输入数据特有的分解方式,增强了算法对AUV非线性信息的提取与学习,提高了速度预测精度。实验结果表明:所提算法能够准确预测DVL失效期间丢失波束的速度,降低组合导航的位置误差,提高了系统的鲁棒性和定位精度。

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。

一种机载惯性/光电组合导航误差修正方法

为提升无人机在全球卫星导航系统受限甚至拒止环境下的导航精度,提出了一种机载惯性/光电组合导航误差修正方法。考虑了捷联惯性导航系统(SINS)姿态误差对光电探测系统(EODS)定位精度的影响,建立了包含SINS姿态误差的测量方程,推导了滤波观测噪声与EODS测距、测角误差之间的函数关系,建立了准确的观测噪声协方差矩阵模型。仿真结果表明:相较于传统SINS/EODS组合导航算法,所提方法滤波收敛速度更快,并且有着更高的姿态和位置精度,2000 s后横滚、俯仰、航向精度分别提高47%、59%、82%,东向、北向、天向定位精度分别提高49%、44%、25%,提升了无人机在卫星信号较弱或缺失的环境下的导航能力。

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。

自适应无迹卡尔曼滤波算法在水下组合导航系统中的应用

【目的】解决水下组合导航系统中先验噪声与实际噪声分布不匹配时,融合滤波性能下降问题,提高自主式水下航行器导航精度。【方法】提出一种自适应无迹卡尔曼滤波算法(AUKF),在融合算法中引入自适应因子;重构系统状态方程中速度项与状态变量的结合方式,解决系统方差不一致问题。通过仿真实验和半物理实验验证该算法的有效性。【结果与结论】与无迹卡尔曼滤波算法相比,在平均位置估计偏差上,AUKF算法的纬度均方根误差(RMSE)降低27%,经度RMSE降低27%,高度RMSE降低25%。AUKF在面对偏差对系统状态的扰动时能够有效抑制滤波发散,从而有效地提高自主式水下航行器的导航精度。

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TEC的石英挠性加速度计组件一体化温控结构热设计

在高精度惯性导航系统和捷联惯导重力测量系统中,石英挠性加速度计组件的温控精度直接影响系统的导航和重力测量精度。为保证加速度计工作环境的温度稳定性,设计了基于半导体制冷器(Thermoelectric Cooler, TEC)的石英挠性加速度计一体化温度控制系统。对系统硬件结构和热特性进行了分析,完成了相关组件的热力学建模、仿真分析和结构优化,并设计了常温及变温环境下的温控实验。相关结果表明,一体化温控系统在2 h室温环境下,加速度计组件温度波动峰-峰值优于0.006℃;在最高10℃/h的快速变温环境下,系统3 h温度控制波动峰-峰值优于0.006℃,加速度计表头温度波动峰-峰值优于0.03℃。对比温控关闭状态下的实验结果,温控开启时,加表输出稳定性及输出精度分别得到了34.2倍和37.6倍的提升。相关结果表明系统有较好的鲁棒性和温控精度,可以满足多种应用环境下的加速度计组件温控应用需求。