基于压电式加速度传感器的船用振动测量仪设计

振动测量在提高船舶动力设备的使用寿命和可靠性方面已经成为船舶工程测试的一项重要课题。为了诊断船舶动力设备的机械故障并监测其运行时振动状态,设计了一种基于压电式加速度传感器,采用差分运算电路与电压并联负反馈电路实现信号的稳定转换,由巴特沃斯滤波电路等进行信号调理的船用振动测量仪。该船用振动测量仪输出的电压信号稳定,满足机械设备振动测量的精度要求,且与市面上大多数的振动分析仪匹配,已在工程中得到了大量应用。     

机载惯性传感器信号降噪研究

针对机载条件下惯性传感器千扰输出是来源多样的非平稳、非线性信号,采用传统降噪方法有明显的不足和局限这一问题,分析了机载惯性传感器输出信号的特点,提出基于局城波分解对实测机载惯性传感器信号降噪.根据实测数据使用局域波分解和小波包变换进行信号降噪对比分析,结果表明局域波分解和小波包变换对于存在噪声干扰的机载惯性传感器信号可以有效降噪并提取有用信号.局域波分解消除惯性传感器信号嗓声计算简便,容易实现.     

硬件在环船舶模拟器的设计与实现

针对在船舶电子设备开发过程中缺乏船舶模拟对象和船用标准信号的问题,设计了硬件在环船舶模拟器.选用ARM单片机LM3S1958作为模拟器的主控芯片,洋细设计了舵角测量电路、通信接口电路和步进电罗经输出电路,分别给出了系统数据流图、主程序框图和主要软件算法.该模拟器可以模拟不同类型的船舶运行状况,输出电罗经、舵角和GPS标准信号,产生阶跃和正弦等扰动信号.该模拟器已成功应用于航向控制器和复示器等船舶电子设备的开发过程中.     

基于ATmega128的泥浆压力脉冲信号仿真器

本文提出了一种基于Atmkega128单片机的某种专用泥浆压力脉冲信号仿真器,该仿真器用于石油钻井无线随钻测量仪器的研制开发和维修过程中.本信号仿真器的核心是ATmega128单片机,通过单片机的软件来模拟各种不同类型的信号和噪声,并且能将这些信号和噪声混合后,通过特殊的硬件电路以4-20毫安电流环的标准工业传感器形式输出.这种仿真器使用简单方便,能够逼真的模拟实际工程现场的压力信号,稳定性好,性能先进.     

基于部分惯性传感器信息的单目视觉同步定位与地图创建方法

传统的单目视觉同步定位与地图创建(MonoSLAM)方法很难处理累积误差问题,如何有效地利用惯性传感器输出的运动信息辅助SLAM系统抑制累积误差是MonoSLAM研究中的一项重要内容.由于惯性传感器输出的三轴方向角中横滚角和俯仰角的精度较高,而偏航角的精度相对较低,如果在SLAM系统中直接使用惯性传感器输出的偏航角信息不但无法有效地抑制该系统中的累积误差,反而会进一步增大系统误差、降低SLAM系统的稳定性.针对这种情况,提出一种基于惯性传感器横滚角和俯仰角的MonoSLAM方法.首先利用惯性传感器输出的横滚角和俯仰角进行系统标定;然后将惯性传感器自身的偏航角作为系统状态向量的一个分量,利用扩展卡尔曼滤波器实时地估计状态向量,进而实现实时鲁棒的同步定位和地图创建.实验结果表明,该方法可以有效地抑制SLAM系统运行过程中产生的累积误差,并降低惯性传感器测量误差对SLAM系统稳定性的影响.     

随机共振在惯性传感器信号处理中的应用

针对实际处理惯性传感器信号的特点,根据随机共振利用噪声增强信号传输的特性,对现有随机共振的算法进行分析和改进,提出将随机共振应用于惯性传感器输出信号处理方法,即将随机共振系统与恢复系统相结合组成随机共振滤波系统,并通过改进系统参数和恢复方法以及对Runge-Kutta算法实现惯性传感器信号的滤波处理。最后分别对惯性传感器的静态数据和动态数据进行验证,结果表明改进的算法能有效地消除干扰,有效地改善了输出信号精度。所提出的滤波方法与传统的滤波方法相比有较大的优势,为惯性传感器信号处理提供了一种新的方法,具有实际应用价值。     

GCC-RIA型船舶操作信息自动记录仪的研制及其系统构成

介绍了一种新问世的简易型船用“黑匣子”—ＧＣＣ－ＲＩＡ型船舶操作信息自动记录仪的研制。采用积木式结构，精选记录信息个数，采用微机技术，设计了一系列不同类型的传感器，研制成功的Ⅰ型船舶操作信息自动记录仪已通过全国航海界权威和省级鉴定。     

基于MEMS传感器惯性测量单元设计与实现

随着微电子机械系统(MEMS)技术的发展,出现了大量高性价比MEMS传感器,被广泛应用于多个领域。然而惯性测量单元通常包括多个传感器,如三轴MEMS加速度、角速度和磁力传感器,这样要求有多个数据通道输出,给原型系统设计和开发带来不便。基于此,本文设计了一种惯性传感板,集成MEMS三轴加速度传感器、三轴角速度传感器和三轴磁力传感器,通过单个UART串口可以读取9通道数据。经过试验验证,该惯性传感单元能够满足一般原型系统的快速设计和开发。     

基于MEMS传感器惯性测量单元设计与实现

随着微电子机械系统(MEMS)技术的发展,出现了大量高性价比MEMS传感器,被广泛应用于多个领域。然而惯性测量单元通常包括多个传感器,如三轴MEMS加速度、角速度和磁力传感器,这样要求有多个数据通道输出,给原型系统设计和开发带来不便。基于此,文中设计了一种惯性传感板,集成MEMS三轴加速度传感器、三轴角速度传感器和三轴磁力传感器,通过单个UART串口可以读取9通道数据。经过试验验证,该惯性传感单元能够满足一般原型系统的快速设计和开发。     

Pickering Interfaces发布PXI毫伏级热电偶仿真器

<正>作为电子测试与仿真领域模块化信号开关产品的领导者,英国Pickering公司面向电子测试测量的广大用户隆重发布第一款毫伏级热电偶仿真器。PXI热电偶仿真器(41-760系列)是用于模拟热电偶工作状态的理想装置。本次发布8、16、24、32共4种通道数量的产品,每个通道通过2个引脚输出微小电压,可选择幅度范围±20 mV、分辨率0.7μV,幅度范围±50mV、分辨率1.7μV或幅度范围±100 mV、分辨率3.3μV。可以覆盖绝大多数常用热电偶的类型。新的热电偶仿真器使用2线输出,配合1条远程探测参考线,在系统存在共模干扰的情况下仍然可以确保准确输出所需的微小电压。此外,每个输出通道还可以设置为输出开路以仿真传感器的故障情况。为了提高输     

New generation precision motion simulators with inertial sensors and digital control

Precision motion simulators (controlled platforms) that use inertial angular rate sensors based on various principles (fiber optic, laser, floated, and other gyros) both for a single-mode simulator operating on the self-testing principle and for a wide range simulator operating in the low speed and high speed modes and equipped (to extend its functional capabilities) with triads of quartz pendulous accelerometers are considered. The problem is in designing digital control systems for such motion simulators that ensure highly accurate stabilization of the prescribed motions of the controlled platform. A solution of this problem is found that allows one to design digital control algorithms for the use with various angular rate sensors. The solutions are analyzed in the time and frequency domains, and the high quality of control is confirmed.     

人体动作识别中的惯性传感器位置校正算法

近年来, 惯性传感器在人体动作识别中的应用受到了广泛关注, 但用户在重新穿戴惯性传感器时, 不能保 证每次的固定位置完全一致, 这会影响识别精度. 针对此问题, 本文提出一种利用旋转矩阵实现惯性传感器位置校 正的人体动作识别方法. 首先, 将惯性传感器按照不同的位置固定在手腕处采集动作数据. 然后, 根据矩阵旋转变换 原理, 通过标准固定位置与其他固定位置的基准数据求取旋转矩阵. 最后, 对动作数据提取时频域特征, 并构造加 权BP神经网络模型以验证校正方法的有效性. 同时还讨论了不同的数据融合方法对动作识别的影响. 结果表明, 校 正后的动作数据的识别准确率分别为84.25%, 85.94%, 相比校正前提高了66.16%, 54.35%, 说明该方法是有效的.     

Recognizing multiple human activities and tracking full-body pose in unconstrained environments

Visual observations, such as camera images, are hard to obtain for long-term human motion analysis in unconstrained environments. In this paper, we present a method for human full-body pose tracking and activity recognition from measurements of few body-worn inertial orientation sensors. The sensors make our approach insensitive to illumination and occlusions and permit a person to move freely. Since the data provided by inertial sensors is sparse, noisy and often ambiguous, we use a generative prior model of feasible human poses and movements to constrain the tracking problem. Our model consists of several low-dimensional, activity-specific manifold embeddings that significantly restrict the search space for pose tracking. Using a particle filter, our method continuously explores multiple pose hypotheses in the embedding space. An efficient activity switching mechanism governs the distribution of particles across the activity-specific manifold embeddings. Selecting a pose hypothesis that best explains incoming sensor observations simultaneously allows us to classify the activity a person is performing and to estimate the full-body pose. We also derive an effective measure of predictive confidence that enables detecting anomalous movements. Experiments on a multi-person data set containing several activities show that our method can seamlessly detect activity switches and accurately reconstruct full-body poses from the data of only six wearable inertial sensors.     

接触式传感器测量软体驱动器角度的数据融合

现有用于软体驱动器角度测量的接触式传感器主要包括惯性传感器与曲率传感器,但惯性传感器的测量精度易受软体驱动器内嵌气道膨胀的影响,曲率传感器测量则存在迟滞和漂移等问题。为进一步提高接触式传感器测量软体驱动器角度的准确性,结合模糊推理与卡尔曼滤波结合的算法实现惯性传感器和曲率传感器数据融合。基于BP神经网络和长短时记忆网络分别融合曲率传感器和惯性传感器,减少接触式传感器测量软体驱动器角度时迟滞和气道膨胀的影响。实验结果显示,采用长短时记忆网络、BP神经网络和模糊推理与卡尔曼滤波相结合的数据融合结果均方根误差精度分别为0.51°、0.63°和1.59°,表明长短时记忆网络能够更好地提高接触式传感器对软体驱动器角度的测量精度。     

Vision and inertial sensor cooperation using gravity as a vertical reference

This paper explores the combination of inertial sensor data with vision. Visual and inertial sensing are two sensory modalities that can be explored to give robust solutions on image segmentation and recovery of 3D structure from images, increasing the capabilities of autonomous robots and enlarging the application potential of vision systems. In biological systems, the information provided by the vestibular system is fused at a very early processing stage with vision, playing a key role on the execution of visual movements such as gaze holding and tracking, and the visual cues aid the spatial orientation and body equilibrium. In this paper, we set a framework for using inertial sensor data in vision systems, and describe some results obtained. The unit sphere projection camera model is used, providing a simple model for inertial data integration. Using the vertical reference provided by the inertial sensors, the image horizon line can be determined. Using just one vanishing point and the vertical, we can recover the camera's focal distance and provide an external bearing for the system's navigation frame of reference. Knowing the geometry of a stereo rig and its pose from the inertial sensors, the collineations of level planes can be recovered, providing enough restrictions to segment and reconstruct vertical features and leveled planar patches.     

利用惯性传感器的人体运动空间轨迹追踪

人体运动的空间轨迹追踪是一种利用传感器技术和计算机技术来分析记录人体的运动过程的方法.为了实现人体运动轨迹的空间追踪,本文设计了一种人体可穿戴式的人体运动捕捉系统,通过佩戴在人体关节点的惯性传感器单元来获取肢体的实时姿态信息.惯性传感器由加速度传感器、角速度传感器和磁力计构成.通过微控制单元获取传感器数据,利用低通滤波和卡尔曼滤波来更新四元数,再将预处理后的数据由蓝牙模块实时发送到电脑端.本文通过对肢体运动的不同角度的实验,证明了利用惯性传感器可以追踪人体肢体、运动的空间轨迹.     

The integration of strap-down INS and GPS based on adaptive error damping

The concept and results of integration of a strap-down inertial navigation system (INS) based on low-accuracy inertial sensors and the global positioning system (GPS) have been presented in this paper. This system is aimed for the purposes of navigation, automatic control, and remote tracking of land vehicles. The integration is made by the implementation of an extended Kalman filter (EKF) scheme for both the initial alignment and navigation phases. Traditional integration schemes (centralized and cascaded) are dominantly based on the usage of high-accuracy inertial sensors. The idea behind the suggested algorithm is to use low-accuracy inertial sensors and the GPS as the main source of navigation information, while the acceptable accuracy of INS is achieved by the proper damping of INS errors. The main advantage of integration consists in the availability of reliable navigation parameters during the intervals of absence of GPS data. The influence of INS error damping coefficients is different depending on the fact whether the moving object is maneuvering or is moving with a constant velocity at that time. It is proposed that INS error damping gain coefficients generally should take higher values always when GPS data are absent, while at the same time their values in the error model (EKF prediction phase) can be additionally adapted according to the actual values of vehicle acceleration. The analysis of integrated navigation system performances is made experimentally. The data are acquired along the real land vehicle’s trajectory while the intervals of absence of GPS data are introduced artificially on the parts characterized both by maneuver and by constant velocity.     

An adaptive constrained type-2 fuzzy Hammerstein neural network data fusion scheme for low-cost SINS/GNSS navigation system

In low-cost micro-electro mechanical system (MEMS)-grade strap-down inertial navigation system (SINS), failure to compensate inertial sensors errors as well as un-modeled uncertainties in SINS could result in exponentially divergence in overall performance of low-cost SINSs. This study deals with the enhancement of low-cost SINS accuracy in combination of global navigation satellite system (GNSS). In this respect, a novel adaptive constrained integrated scheme for SINS/GNSS is developed based on type-2 fuzzy Hammerstein neural network (T2FHNN). To this aim, a gray-box Hammerstein neural network model are defined based on clear interpretation with the physical nature of the inertial sensors error. In addition a knowledge-based type-2 fuzzy programming extracted from inertial sensors data is also used for managing the learning rate of Hammerstein neural networks. Some vehicular real-world tests have been carried out in order to show the effectiveness and feasibility of the proposed integration scheme in the long-term performance and accuracy of the proposed navigation algorithm. The results indicate that the proposed integration algorithm improved the navigation accuracy, reliability and stability in the presence of state constraints of the stand-alone SINS during signal blockage of GNSS.     

微惯性测量单元设计及其误差补偿模型的研究

针对微惯性测量单元(MIMU)小体积、低功耗、低成本、高实时性的应用需求,设计了一种基于ARM和MEMS惯性器件的MIMU系统,并根据实验中得到的惯性器件的误差特性建立了一种惯性器件误差补偿模型,然后在硬件系统上进行了实验验证.利用该模型对惯性器件测量结果进行修正,可以有效抑制误差,提高MIMU的测量精度.整个系统能满足使用精度要求.     

On-line tracking simulator with a hybrid of physical and Just-In-Time models

A tracking simulator that runs in parallel with the plant and exactly simulates dynamic plant behavior has been developed. This tracking simulator is currently in the validation phase at a commercial petrochemical plant site. The future prediction function of the tracking simulator is expected to have a major impact on plant operations, improvement, and control design. The tracking simulator is strongly dependent on the physical model based on physical and chemical laws, and cannot simulate unknown phenomena in the plant. A hybrid model consisting of a physical model and a statistical model based on historical plant operation data was proposed. The estimation accuracy of the statistical model was improved using the virtual input from the tracking simulator. Although the Kalman filter technique is applied to conventional software sensors for quality estimation as a model updating function, deterioration of estimation accuracy is often seen with marked changes in plant behavior and the analyzers shut down. To resolve these issues, we applied Just-In-Time modeling based on the historical data modeling technique with the tracking simulator, and the plant model coverage of the tracking simulator was expanded for various processes.