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.     

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。     

三轴数字MEMS加速度计现场标定方法

微机电系统(micro electro mechanical systems,简称MEMS)加速度传感器作为低成本惯性测量单元在物体姿态监测中有着广泛应用。根据三轴数字加速度传感器的输出数学模型,详细推导了如何计算数学模型中标度因数、安装误差系数以及零偏值。提出一种基于长方体的六位置简单标定方法,对比三轴转台精确标定结果表明六位置简单标定法简单易行,精度较高,易于单片机实现,适合不具备三轴转台的场所,且该方法对MEMS三轴数字加速度计的校准具有很好的通用性。     

考虑横向灵敏度的三轴加速度传感器标定方法研究

横向灵敏度是单轴加速度传感器的重要指标之一,它直接影响到三轴加速度传感器的标定模型与测量精度.本论文以三轴MEMS加速度传感器为研究对象,在传统的标定模型中引入了导致测量耦合的横向灵敏度误差,建立了一种新的三轴加速度传感器标定模型,该模型将传感器的横向灵敏度以对称矩阵的形式引入现有的标定公式,构建了包含零位偏差、标度因...     

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.     

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。     

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。     

Biomimetic control for adaptive camera stabilization in driver-assistance systems

Throughout the last decade, a large number of electronic driver-assistance systems have been developed in order to improve vehicles’ comfort and safety. Up to now, they more or less all rely on odometric data. However in the future, a new form of information is likely to be used: visual sensors, e.g. cameras, can provide additional knowledge on traffic and the vicinity. This paper presents an inertially stabilized camera platform for high precision tracking of points of interest, that has been developed within the project FORBIAS (Research cooperation for biomimetic assistance systems). The inertial stabilization is based on a specially designed sensor system that uses MEMS components in connection with a biologically inspired control algorithm for adaptive enhancement and online calibration of the gaze stabilization system.     

基于MEMS传感器的起重机吊钩运动实时监控系统

针对起重机吊钩运动状态的实时监控问题,设计并实现了一种基于MEMS传感器的吊钩运动监控系统。利用微型加速度计和磁强计实时测量了吊钩的二维倾角和水平方位角;分析了多种不同的磁强计补偿方法,针对强磁环境下水平方位角误差较大的特点,提出了一种基于T-S模糊逻辑的吊钩旋转角误差补偿方法;研制了系统样机,并进行了性能测试。研究结果表明,起重机吊钩倾角测量精度达到0.3°,平面旋转角精度达到0.8°,可满足实际应用要求,具有良好的应用前景。     

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.     

低成本MEMS陀螺仪的复合标定

由于低成本MEMS器件本身具有误差,且初始误差比较大,所以在使用前必须进行标定。在十二位置标定方法的基础上,进行三耦合方位动态实验,标定MIMU陀螺仪的零偏、刻度因数、安装误差系数和与角速度耦合二次有关项系数。将该方法应用于实验室自制MIMU的标定中,标定结果与MTI计进行对比,验证了该标定方法的可行性。     

基于改进卡尔曼滤波的叉车载重快速准确估计方法

针对工作环境恶劣、操作工况复杂的伸缩臂叉车载重实时快速准确估计需求,对伸缩臂叉车的载重估计数学模型及其求解算法进行了研究。首先,利用叉车现有功能模块中已装配的各类传感器,提出并分析了三种载重估计方案,在综合比较各方案的优缺点之后,确定并建立了基于动力学原理的载重估计数学模型;然后,将载重作为估计系统的状态变量,将液压系统压力、臂架变幅角度和伸缩臂伸缩长度等实时信号作为测量值,将基于转动定律建立的载重计算公式作为状态变量与测量值之间的观测方程,运用卡尔曼滤波算法对该数学模型进行求解;同时,为解决卡尔曼滤波算法在递推过程中状态变量发生改变从而导致大量新测量数据对状态变量失去校正能力的问题,提出了一种基于改进卡尔曼滤波的载重估计算法;最后,对某企业超长载重伸缩臂叉车进行了不同载重的离线试验和在线试验。研究结果表明：对于454 kg的轻载荷,该算法的估计结果的最大绝对误差小于91 kg,而对于1100 kg、2268 kg、3368 kg和4536 kg的重载荷,其平均绝对百分比误差小于3%;趋于稳定估计值的响应时间可在1 s之内,完全优于实际应用需求。该方法模型简单、可移植强,可推广应用到起...     

基于单星测量的星光惯性组合导航

为减小杂光影响,全天时星光定向仪一般采用小视场,同一时刻只能观测一颗恒星无法输出姿态信息.本文提出一种基于单星测量的星光惯性组合导航系统,首先根据惯导输出的姿态和位置信息,控制转台和星光定向仪摆镜保证星光定向仪对目标导航星的观测,之后根据惯导的误差模型建立系统状态方程,根据星光定向仪测量的导航星方向矢量建立测量方程,利...     

温湿度传感器检定装置对比实验与分析

合理选取HMP45D温湿度传感器的检定装置对提高校准质量、节约校准成本具有重要意义。文中基于本单位现有的4台温湿度检定装置,通过大量的对比实验,分析了不同装置对HMP45D温湿度传感器校准质量的差异。结果表明：恒湿盐湿度发生器和DJM10型湿度检定箱具有较高的性价比,适合作为HMP45D温湿度传感器的检定装置。具体来说,恒湿盐湿度发生器适合HMP45D温湿度传感器的现场校准工作,而湿度检定箱更适合完成室内校准任务。     

Demonstration of robust micromachined jet technology and its application to realistic flow control problems

This paper describes the demonstration of successful fabrication and initial characterization of micromachined pressure sensors and micromachined jets (microjets) fabricated for use in macro flow control and other applications. In this work, the microfabrication technology was investigated to create a micromachined fluidic control system with a goal of application in practical fluids problems, such as UAV (Unmanned Aerial Vehicle) -scale aerodynamic control. Approaches of this work include: (1) the development of suitable micromachined synthetic jets (microjets) as actuators, which obviate the need to physically extend micromachined structures into an external flow; and (2) a non-silicon alternative micromachining fabrication technology based on metallic substrates and lamination (in addition to traditional MEMS technologies) which will allow the realization of larger scale, more robust structures and larger array active areas for fluidic systems. As an initial study, an array of MEMS pressure sensors and an array of MEMS modulators for orifice-based control of microjets have been fabricated, and characterized. Both pressure sensors and modulators have been built using stainless steel as a substrate and a combination of lamination and traditional micromachining processes as fabrication technologies.     

Adaptive complementary filter using fuzzy logic and simultaneous perturbation stochastic approximation algorithm

This paper addresses the problem of attitude estimation using low cost, small-sized inertial sensors under dynamic maneuvers. An adaptive complementary filter with fuzzy logic and simultaneous perturbation stochastic approximation (SPSA) algorithm is proposed. By recognizing the situation of dynamic condition via fuzzy logic, the cut-off frequency of the complementary filter is determined adaptively under varying vehicle dynamics. Also, the SPSA algorithm is used to tune the parameters of fuzzy system. Simulation results based on the test data show that the proposed SPSA-based fuzzy complementary filter exhibits a significant performance improvement for attitude estimation during dynamic maneuvers.     

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。     

Soft sensor for real-time cement fineness estimation

This paper describes the design and implementation of soft sensors to estimate cement fineness. Soft sensors are mathematical models that use available data to provide real-time information on process variables when the information, for whatever reason, is not available by direct measurement. In this application, soft sensors are used to provide information on process variable normally provided by off-line laboratory tests performed at large time intervals. Cement fineness is one of the crucial parameters that define the quality of produced cement. Providing real-time information on cement fineness using soft sensors can overcome limitations and problems that originate from a lack of information between two laboratory tests. The model inputs were selected from candidate process variables using an information theoretic approach. Models based on multi-layer perceptrons were developed, and their ability to estimate cement fineness of laboratory samples was analyzed. Models that had the best performance, and capacity to adopt changes in the cement grinding circuit were selected to implement soft sensors. Soft sensors were tested using data from a continuous cement production to demonstrate their use in real-time fineness estimation. Their performance was highly satisfactory, and the sensors proved to be capable of providing valuable information on cement grinding circuit performance. After successful off-line tests, soft sensors were implemented and installed in the control room of a cement factory. Results on the site confirm results obtained by tests conducted during soft sensor development.     

混合式惯导系统自标定技术研究

混合式惯导系统是一种集平台式、捷联式、旋转式惯导系统优点于一体的新型惯导系统。该型惯导可利用自身的旋转机构和高精度、高分辨率角度传感器实现不拆机条件下的误差参数自标定功能,极大简化了标校流程和系统维护工作量,有利于武器平台和作战系统效能的发挥。针对混合式惯导系统的结构特点和典型的旋转方式进行了误差分析,给出了误差参数自标定的设计原则和方法,并用混合式惯导原理样机进行了验证,结果表明,所设计的自标定方案能够在不拆机条件下利用自身旋转机构完成对陀螺漂移、加计零偏、刻度系数误差及安装偏角等参数进行精确估计,因此具有较高的理论意义和工程应用价值。     

仿生偏振视觉定位定向机理与实验

为了有效利用全天域的偏振光信息,探究仿生偏振光导航机理,设计了偏振视觉传感器。介绍了基于四相机的偏振视觉传感器及其标定方法,推导了冗余配置下偏振态的最小二乘估计算法。分析了基于一阶瑞利散射模型的天空光偏振模式,将太阳方向矢量的最优估计问题转化为求解矩阵的特征向量问题,推导出了基于天空光偏振模式的定位定向算法。最后,设计了静态实验与转动实验,对理论分析结果进行了验证。实验结果显示:测量的天空光偏振模式与瑞利散射模型相一致,并可从中成功提取太阳方向矢量。静态实验测量的太阳天顶角的最大误差约为0.4°,误差标准差为0.14°;基于1h对天空偏振光的观测数据实现的定位误差为68.6km。转动实验(转动两周)得到的最大定向误差约为0.5°,误差标准差为0.28°。研究结果揭示了生物利用偏振光导航的机理,为仿生偏振光导航的应用提供了理论依据。