小型飞行器MEMS姿态测量系统

针对目前可获得的微机电系统(micro electromechanical system,简称MEMS)惯性测量元件,提出一种用于小型飞行器的姿态测量系统实现方案,采用三轴加速度计和单轴速率陀螺构建系统,可满足飞行器加速度小于6g、角速度小于±300(°)/s的姿态测量需求。根据所选MEMS惯性传感器的特点,给出传感器的测试方案和测试结果,利用加速度计测量信息直接修正方向余弦矩阵来抑制姿态角的误差积累,并进行姿态测量试验。试验结果表明:系统以100Hz的频率更新姿态测量值,在满足姿态矩阵修正阈值的条件下,姿态测量误差小于1(°)。     

基于STM32的多惯性测量单元数据采集系统设计

针对单个MEMS惯性测量单元存在积累误差的问题,开发了一套基于STM32的多惯性测量单元数据采集系统,为后续研究提供真实可靠的数据源。该系统主要包括数据采集、数据实时显示和数据存储三个功能模块。数据采集模块利用STM32单片机实现了多个惯性测量单元的数据采集与融合;数据实时显示模块和数据存储模块完成了对多个惯性测量单元输出的加速度、角速度和姿态数据的实时显示与自动存储。经过多次测试,结果表明该系统性能稳定可靠,可用于工程实践中。     

MEMS音叉式振动陀螺仪的动态特性研究

MEMS陀螺仪是MEMS惯性测量组合单元中的重要组成部分,其精度的高低直接影响到整个组合单元的输出精度.本文介绍了MEMS音叉式振动陀螺仪的结构特点及工作机理,主要建立了其误差模型,对原理性误差进行了定性分析,给出了影响其精度的主要干扰源,并提出了误差补偿的方法.     

MEMS惯性开关的系统级仿真

提出一种基于静电控制,具有阈值可调功能的微电子机械系统(MEMS)惯性开关。采用CoventorWare软件中的Architect模块对MEMS惯性开关进行系统级建模与仿真。分析了多物理耦合场下MEMS惯性开关的动态响应特性,得到阈值加速度和电压的对应关系曲线。结果表明:通过改变初始电压,可以调节开关阈值大小;对开关进行了系统级的模态分析和谐响应分析,MEMS惯性开关的工作频率范围在22.901 kHz以内且具有抗击104g正弦加速度的能力。     

MEMS安全系统后坐保险机构设计

针对现有MEMS安全系统中后坐保险机构结构过于繁杂而带来加工不便、试件工艺过程应力复杂的问题,提出可用于MEMS安全系统的微惯性销后坐保险机构。通过理论分析与有限元分析相结合的研究方法验证微惯性销后坐保险机构的可行性。仿真结果表明,微惯性销后坐保险机构可以实现勤务跌落和正常发射环境的区分。并通过将微惯性销斜置,可以减小惯性销响应位移,使MEMS安全保险系统轴向更紧凑,符合MEMS安全系统对隔爆执行件轴向小尺寸的要求。相对于滑块式、悬臂梁式后坐保险机构,微惯性销后坐保险机构明显降低了结构复杂度以及改善了机构的加工性能。     

基于水银的电容式加速度计研究

探索一种基于新原理的加速度计,从根本上解决加速度计在弹性结构设计方面高灵敏度和抗高过载之间存在的矛盾;利用微滴水银作为对加速度敏感的弹性电极与硅片上电极构成电容式硅微加速度计,并可利用MEMS工艺来实现;通过建模仿真,水银电容加速度计的灵敏度要高于传统的MEMS电容加速度计;这种加速度计具有结构简单、对硅微加工工艺要求低、易于实现三维加速度测量的突出优点;对于冲击环境下的惯性测量有不可替代的作用.     

微惯性测量单元的误差整机标定和补偿

提出了微惯性测量单元(MIMU)在高动态、高过载复杂应用条件下的误差整机标定和补偿方法.首先,建立了高动态,高过载复杂应用条件下MIMU的误差模型,该模型包括了结构误差,传感器安装误差和MEMS惯性传感器在复杂条件对精度影响较大的误差项,指零位温度漂移、互耦误差、刻度因子非线性和微陀螺加速度效应误差;根据模型提出了整机...     

利用MEMS综合微加工方法实现的非全硅微陀螺技术

本文的主要内容包括(1)简要介绍了硅微型陀螺仪的分类及其基本工作原理,包括硅微型梳状线振动驱动式陀螺仪、振动轮式微机械陀螺仪、硅微型框架驱动式陀螺仪.(2)详细介绍了以微陀螺为核心的微型惯性测量系统的组成,在应用中的关键技术,主要问题及解决途径,并展望了微型惯性测量系统的发展前景.(3)提出了利用MEMS综合微加工方法实现的非全硅微陀螺技术.     

MEMS加速度传感器在微位移测量中的应用研究

MEMS加速度传感器在微位移测量中有着广泛的应用,文章介绍了目前世界上主要的MEMS加速度传感器研究和生产情况,并针对MEMS加速度传感器在微位移测量方面的应用进行了概述,对其应用领域、研究方法和得出的结论进行了分析与评述,提出了目前存在的问题,并给出了相关的建议,为MEMS加速度传感器的进一步研究和应用提供参考。     

管道地理坐标内检测的里程校正算法

利用管道清管器(pipeline cleaner,PIG)搭载捷联惯导(strap down intertial navigation system,SINS)器件实现管道地理坐标的定期测量.SINS计算的结果发散是测量系统实用化的瓶颈,为此将PIG里程校正的卡尔曼(Kalman)滤波方法应用于管道地理坐标测量中.根据管道内检测环境的需要选择霍尔式里程轮和采用微机电系统(micro-electromechanical systems,MEMS)的惯性测量单元(inertial measurement unit,IMU);针对MEMS器件精度较低的问题,利用分离滤波方法减小信号的零偏噪声,使惯性信号满足Kalman滤波的需要;建立了9维状态误差的数学模型,将里程轮的速度与SINS计算的速度之差作为观测量,通过Kalman滤波对管道定位的状态误差进行估计和补偿;搭建实验装置进行实验验证.结果表明,校正算法解决了计算结果发散的问题,检测160 m长度的管道精度达到3.8％,具有一定的实用性.     

High accuracy navigation information estimation for inertial system using the multi-model EKF fusing adams explicit formula applied to underwater gliders

The underwater navigation system, mainly consisting of MEMS inertial sensors, is a key technology for the wide application of underwater gliders and plays an important role in achieving high accuracy navigation and positioning for a long time of period. However, the navigation errors will accumulate over time because of the inherent errors of inertial sensors, especially for MEMS grade IMU (Inertial Measurement Unit) generally used in gliders. The dead reckoning module is added to compensate the errors. In the complicated underwater environment, the performance of MEMS sensors is degraded sharply and the errors will become much larger. It is difficult to establish the accurate and fixed error model for the inertial sensor. Therefore, it is very hard to improve the accuracy of navigation information calculated by sensors. In order to solve the problem mentioned, the more suitable filter which integrates the multi-model method with an EKF approach can be designed according to different error models to give the optimal estimation for the state. The key parameters of error models can be used to determine the corresponding filter. The Adams explicit formula which has an advantage of high precision prediction is simultaneously fused into the above filter to achieve the much more improvement in attitudes estimation accuracy. The proposed algorithm has been proved through theory analyses and has been tested by both vehicle experiments and lake trials. Results show that the proposed method has better accuracy and effectiveness in terms of attitudes estimation compared with other methods mentioned in the paper for inertial navigation applied to underwater gliders.     

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。     

融合自适应卡尔曼和小波的MEMS陀螺去噪方法

为提高MEMS陀螺仪信号的测量精度,提出一种融合卡尔曼和小波的MEMS陀螺仪自适应抗野值去噪方法。卡尔曼滤波中根据信息对干扰数据进行实时检测,通过修正增益或状态的一步预测值抑制野值对滤波精度的影响,然后利用小波分析对滤波后的陀螺仪信号的低频、高频分量同时进行阈值处理。实验表明该方法去噪效果优于卡尔曼滤波和Visushrink,陀螺仪x、y、z轴零偏不稳定性在该方法下比卡尔曼滤波分别提高了31.0%、29.3%、30.5%,比Visushrink分别提高了2.4%、12.1%、12.4%。     

Distributed observers for pose estimation in the presence of inertial sensory soft faults

Distributed Particle-Kalman Filter based observers are designed in this paper for inertial sensors (gyroscope and accelerometer) soft faults (biases and drifts) and rigid body pose estimation. The observers fuse inertial sensors with Photogrammetric camera. Linear and angular accelerations as unknown inputs of velocity and attitude rate dynamics, respectively, along with sensory biases and drifts are modeled and augmented to the moving body state parameters. To reduce the complexity of the high dimensional and nonlinear model, the graph theoretic tearing technique (structural decomposition) is employed to decompose the system to smaller observable subsystems. Separate interacting observers are designed for the subsystems which are interacted through well-defined interfaces. Kalman Filters are employed for linear ones and a Modified Particle Filter for a nonlinear non-Gaussian subsystem which includes imperfect attitude rate dynamics is proposed. The main idea behind the proposed Modified Particle Filtering approach is to engage both system and measurement models in the particle generation process. Experimental results based on data from a 3D MEMS IMU and a 3D camera system are used to demonstrate the efficiency of the method.     

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.     

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.     

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.     

状态χ2检验法在惯性航姿系统内阻尼卡尔曼滤波器中的应用

本文探讨了如何利用惯性测量组合本身的信息来提高捷联航姿系统的姿态精度。根据平台式阻尼网络的思想，设计了捷联式内阻尼卡尔曼滤波器，将惯导系统捷联解算获得的姿态与加速度计估计的姿态进行组合，在系统非加速度状态下，提高了姿态输出的精度。为了实时监测系统的运动状态从而判断内阻尼姿态的有效性，本文成功将状态χ2检验法应用在内阻尼卡尔曼滤波器中，设计了基于2个状态传播器的故障监测器，并通过对故障检测向量元素的检验代替对整个向量的检验，提高了故障监测的灵敏度和可靠性。最后，实际系统的动静态实验验证了本文所提出的方法的有效性。     

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。     

A novel Kalman filter for combining outputs of MEMS gyroscope array

In this paper, a Kalman filter for combining outputs of a gyroscope array is presented to improve the accuracy of microelectromechanical system (MEMS) gyroscope. A theoretical mathematical model for the accuracy improvement is described. Especially, a discrete-time filter is designed by solving the covariance differential equation with an analytic solution. Performances of presented filter are analyzed by the simulations. Finally, a developed system consisting of six-gyroscope array is implemented to test the performance of the Kalman filter. The experimental results showed a noise density of 0.03°/s/√Hz for the combined rate signal compared to the 0.11°/s/√Hz for the individual gyroscope in the array. The analysis of results measured from Allan variance demonstrated a bias instability of 17.2°/h and angular random walk of 1.6°/√h, whereas the corresponding values for the individual gyroscope is 62°/h and 6.2°/√h, respectively. It proved that the presented approach is effective to improve the MEMS gyroscope accuracy.