Characterization and calibration of MEMS inertial sensors for state and parameter estimation applications

Widening applications of inertial sensors have triggered the search for cost effective sensors and those based on MEMS technology have been gaining popularity and widespread use particularly for lower cost applications. However, inertial sensors are subject to various error sources and characteristics of these should be modelled carefully. Corrective calibration is required for successful use for anything but the most trivial applications, body state estimation and navigation being important application areas. In this paper, we review the deterministic error and random noise sources for these sensors, consider a number of inertial sensor calibration tests to provide models for these errors and derive the calibration parameters for MEMS based strapdown IMUs. We carry out these tests and present the results for a low cost and popular IMU. We further provide performance results for an example application of body state and parameter estimation using the derived calibration data and discuss our results.     

抗野值自适应卫星/微惯性组合导航方法

针对在复杂城市环境下卫星导航系统(GNSS)定位定速存在野值,导致GNSS/微惯性(MEMS-INS)组合导航状态参数滤波估计精度恶化,甚至滤波发散的问题,提出了一种抗野值自适应GNSS/MEMS-INS组合导航算法,以提高组合导航精度和可靠性。该算法利用Allan方差分析建立较为精确的MEMS器件噪声模型,有效降低模型异常和状态扰动的影响。同时利用新息序列构造观测异常检验统计量,并根据该统计量构造自适应新息加权因子调节滤波增益矩阵,削弱观测野值对状态估计的不良影响。实验结果表明,该算法能够有效地控制GNSS定位定速异常的影响,具有较强的实时性和容错性。相比于传统算法,车载定位、定速和定姿精度分别提升35.78%、60.19%和82.41%,验证了本文算法的有效性和实用性。     

不依赖精密转台的 MEMS-IMU 误差标定补偿方法

微机电惯性测量单元(MEMS-IMU)具有尺寸小、重量轻、成本低、可靠性高等优点,在机器人、虚拟现实以及智能穿戴等诸多领域广泛应用。低成本的微机电惯性测量单元在使用过程中受噪声和零偏误差等影响,需要通过测试和误差补偿手段来提高其实际使用精度。本文提出了一种全面测试和补偿惯性测量单元误差的方法,通过建立MEMS-IMU的误差模型,使用优化方法标定误差模型中的系统误差参数;使用Allan方差分析方法确定随机误差参数;基于上述结果,采用与视觉融合的非线性优化方法在线实时估计并补偿零偏,最终达到提高定位精度的目的。通过实验分析,上述组合方法不需要使用专门测试标定设备,能够有效补偿低成本微机电惯性测量单元的误差,提高定位精度。     

融合 WiFi 与可穿戴惯导模块的室内定位方法

为解决基于智能手机的人员室内定位追踪易受手机姿态影响的问题,提出一种融合WiFi与可穿戴惯导模块的室内定位方法。通过固定在胸部的惯性测量单元实现行人航迹推算PDR)定位,消除手机姿态对PDR定位的影响,采用加权贝叶斯算法实现WiFi指纹定位,为PDR提供初始定位,同时基于无迹卡尔曼滤波融合WiFi定位结果与PDR定位结果,以减少PDR的累积定位误差。最后,在真实室内环境中进行大量实验,实验结果证明本文提出的加权贝叶斯WiFi定位算法相比于传统贝叶斯算法定位误差降低了51.9%,提出的融合WiFi与可穿戴惯导模块的定位方法具有更好的精度和稳定性,相比于纯PDR定位算法平均定位误差降低了65.2%,相比于完全利用手机实现的融合算法,在3种不同手机姿态下平均定位误差分别下降了12.3%、39.3%和48.4%。     

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.     

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.     

捷联惯导系统的误差模型与仿真

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,简化了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 s导航试验数据.结果表明:导航系X轴的相对系统误差＜20%,Y轴、Z轴的相对系统误差＜4%,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的基本影响.     

基于原子干涉技术的水下重力辅助导航研究展望

重力辅助惯性导航是当前水下潜航器导航定位研究的热点和前沿问题,有望成为下一代水下高精度导航系统发展的重要方向。首先,介绍了水下重力信息对于校正惯导系统误差的重要性,阐述了水下重力辅助惯性导航的基本原理与技术内涵;然后,从无图匹配、有图匹配等不同发展阶段,总结了基于传统相对重力仪的水下重力辅助导航的研究现状及发展趋势;进一步分析了下一代水下自主导航系统对高精度绝对重力测量技术的需求,梳理并讨论了基于原子干涉重力测量技术的最新发展及应用状况,展望了原子干涉重力测量技术在水下惯性导航领域的应用前景并总结了仍需解决的关键技术;最后,给出了我国重力辅助导航研究存在的不足及发展建议。     

ENHANCING KALMAN FILTERING–BASED TIGHTLY COUPLED NAVIGATION SOLUTION THROUGH REMEDIAL ESTIMATES FOR PSEUDORANGE MEASUREMENTS USING PARALLEL CASCADE IDENTIFICATION

Integrated global positioning system (GPS) solutions that utilize micro-electro-mechanical systems (MEMS)-based inertial sensors provide a more accurate navigation solution than stand-alone GPS in challenging scenarios. To keep the integrated solution less affected by sensor errors and to decrease the cost, a reduced inertial sensor system (RISS), which consists of only one gyroscope and two accelerometers, together with an odometer and integrated with GPS, is proposed. Tightly coupled integration is a better choice in demanding scenarios, as it can provide GPS aiding even when the number of visible satellites is three or less. However, inaccuracies of pseudoranges measured by the GPS receiver and used as aiding in the RISS/odometer/GPS integration solution will affect the overall positioning accuracy. This article explores the benefits of using parallel cascade identification (PCI), a nonlinear system identification technique that improves the overall navigation solution by modeling residual pseudorange correlated errors to be used by a Kalman filter (KF)–based tightly coupled RISS/odometer/GPS navigational solution. When less than four satellites are visible, the identified parallel cascade model for the still visible satellites is used to predict the residual pseudorange errors for these respective satellites, and the corrected pseudorange value is provided to KF. The performance of PCI for correcting the pseudoranges is examined and verified using road test trajectories and compared to a traditional tightly coupled RISS/odometer/GPS KF solution. The results demonstrate the advantages of this technique in correcting the pseudoranges and enhancing the positional solution.     

一种基于图优化的行人协同定位方法

基于手机惯性传感器的行人航位推算方法是行人导航的核心方法之一。 然而由于传感器噪声等因素,航位推算获取 的位置信息误差往往随着时间发散,通常将航位推算和卫星导航通过卡尔曼滤波构成组合导航系统,利用卫星提供的高精度定 位信息补偿航位推算误差。 提出一种基于图优化的行人协同定位方法,将状态转移、量测和协同测距信息都作为状态的约束, 统一进行优化估计。 为验证方法的有效性,分别在卫星信号良好、无卫星环境下进行了实验验证。 实验分析结果表明,基于图 优化的行人协同定位方法在有无卫星信号情况下,都可以有效地提升系统的定位精度。 和基于卡尔曼滤波的协同方法相比,最 大水平定位误差都减少了 30% 以上。     

基于改进 ESKF 的植保无人机时延位姿补偿算法

为解决全球导航卫星系统和惯性测量单元融合时间不同步问题,提高植保无人机位姿估计精度,本文根据植保无人机 大惯性、强振动的特性提出一种基于改进误差状态卡尔曼的时延位姿补偿算法。 首先对名义状态变量线性预测,引入渐消因子 提高强振动环境下的系统稳定性;接着采用互补滤波对角速度补偿,对姿态误差状态变量修正;最后结合测量的延迟时间,使用 互补滤波外推数据,提高大惯性特性下的速度位置精度。 实验结果表明,相较于误差状态卡尔曼算法,横滚角和俯仰角均方根 误差减少 0. 266 9°和 0. 241 4°,偏航角均方根误差减少 0. 076 4°;正常航迹植保作业下,东北天方向速度均方根误差减少 0. 210 5、0. 184 9、0. 238 8 m/ s;东北天方向位置均方根误差分别减少 0. 21、0. 19、0. 23 m,有效提高位姿估计精度。     

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.     

卡尔曼滤波在MEMS惯性姿态测量中的应用

由于测控成本和有效载荷的限制,一般采用微机电系统(MEMS)惯性传感器来测量小型无人机的飞行姿态。在MC9S12XS128单片机上通过嵌入式软件编程实现了卡尔曼滤波算法,并在JZJ-1型自准直仪转台上对MEMS加速度计和陀螺仪的输出信号进行了数据融合试验,较好地解决了MEMS惯性测量系统的零漂和机械振动干扰问题。     

基于IMU旋转的捷联惯导系统自补偿方法

为了有效地抑制惯性器件常值偏差对惯导系统导航精度的影响,提出了基于惯性测量单元(inertial measurement unit,IMU)旋转的自动补偿方法.由于旋转的引入,IMU中陀螺仪和加速度计的常值偏差被调制成正弦信号,通过积分运算可以有效地消除常值偏差对惯导系统导航精度的影响.在分析单、双轴旋转补偿原理的基础上,提出一种改进的单轴旋转调制方法并对该方法进行了理论证明和实验分析.与以往的单轴旋转方式及未采用旋转方式时的导航误差进行了比较,结果表明该方案可以消除所有方向上惯性器件常值偏差的影响,有效地提高系统的定位精度.     

Improving unmanned ground vehicle navigation by exploiting virtual sensors and multisensor fusion

This paper investigates techniques on improving navigation accuracy using multiple sensors mounted on a mobile platform and exploiting the inherent characteristic of a ground vehicle that does not move along the cross-track and off the ground, often termed nonholonomic constraints (NHC) for car-like vehicles that assume no slip or skid. The forward velocity of the vehicle is obtained using a wheel encoder. The 3D velocity vector becomes observable during the normal moving state of the vehicle by using NHC, which produces one virtual sensor. Another virtual sensor is the zero-velocity update (ZVU) condition of the vehicle; when the condition is true, the 3D velocity vector (which is zero) becomes observable. These observables were employed in an extended Kalman filter (EKF) update to limit the growth of inertial navigation system error. We designed an EKF for data fusion of inertial measurement units, global positioning systems (GPS), and motion constraints (i.e., NHC and ZVU). We analyzed the effects of utilizing these constraints on improving navigation accuracy in stationary and dynamic cases. Our proposed navigation suite provides reliable accuracy for unmanned ground vehicle applications in a GPS-denied environment (e.g., forest canopy and urban canyon).     

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。     

面向室内行人的Range-only UWB/INS紧组合导航方法

为了提高室内行人组合导航系统的精度和灵活性,提出了一种Range-only超宽带(UWB)/惯性导航系统(INS)紧组合导航方法。该方法将锚点的位置信息引入到系统状态变量中并通过数据融合滤波器进行预估,以克服传统UWB/INS紧组合导航模型中需要预先获取锚点位置信息的缺点,减少锚点位置信息精度对组合导航系统的影响。在此基础上,利用迭代扩展卡尔曼滤波(IEKF)来完成组合导航系统的数据融合滤波,以提高对目标行人导航信息的预估精度。实验结果显示,所提方法的平均绝对位置误差与传统UWB/INS紧组合方法相比降低了11.69%,算法对锚点位置信息的依赖程度也显著降低。     

Nonlinear unbiased minimum-variance filter for Mars entry autonomous navigation under large uncertainties and unknown measurement bias

High-precision navigation algorithm is essential for the future Mars pinpoint landing mission. The unknown inputs caused by large uncertainties of atmospheric density and aerodynamic coefficients as well as unknown measurement biases may cause large estimation errors of conventional Kalman filters. This paper proposes a derivative-free version of nonlinear unbiased minimum variance filter for Mars entry navigation. This filter has been designed to solve this problem by estimating the state and unknown measurement biases simultaneously with derivative-free character, leading to a high-precision algorithm for the Mars entry navigation. IMU/radio beacons integrated navigation is introduced in the simulation, and the result shows that with or without radio blackout, our proposed filter could achieve an accurate state estimation, much better than the conventional unscented Kalman filter, showing the ability of high-precision Mars entry navigation algorithm.     

水下潜航器的惯导/超短基线/多普勒测速信息融合及容错验证

针对水下潜航器惯导系统的定位误差积累和容错性差等问题,分析了水声超短基线的相位差定位方法,推导了基于惯导提供实时位置、姿态误差角信息的惯导/超短基线(INS/USBL)导航解算过程及其坐标转换。结合惯导/多普勒测速(INS/DVL)滤波器,给出INS/USBL/DVL组合导航联邦滤波在3种信息融合算法下的应用。通过MATLAB仿真对导航算法进行了验证,结果表明该导航算法能够抑制惯导系统误差随时间发散的问题,能充分利用了3种导航系统提供的参数信息,且状态维数低,滤波收敛速度快,其中基于精度因子信息分配方法的导航系统误差最小。容错性验证结果显示,当超短基线出现故障时,重构后的组合导航系统在较高航速情况下依旧能提供有效的导航参数。所提出的INS/USBL/DVL组合导航联邦滤波方法能够精确地提供水下潜航器的各位导航参数信息,且具有较高的容错性和稳定性。     

MEMS传感器随机误差Allan方差分析

MEMS传感器中随机误差较大,有时会覆盖传感器中有用信号,提出采用Allan方差(Allan variance)方法对MEMS传感器实测数据进行分析,系统地分析了引起MEMS传感器误差的随机噪声种类及其来源和特性,确定其各项系数,根据系数获得其功率谱密度,根据功率谱密度分析法与Allan方差分析法获得对应各项随机误差的数学模型,然后以数学表达式的形式得到统一的数学模型,再与卡尔曼滤波相结合得到增强的卡尔曼滤波,最后通过车载实验对MEMS-INS/GPS各个姿态进行卡尔曼滤波与改进后卡尔曼滤波2种滤波方法的比较,实验结果表明新滤波方法能很好地提高微惯性系统各个姿态精度.