Digital Readout System for Micromachined Gyroscope and Analysis for its Demodulation Algorithm

A new digital readout system for micromachined gyroscope has been proposed to implement flexible parameter adjustment, improve the control performance of gyroscope, and make error compensation. By digitalizing the output of the gyroscope, this system uses a floating-type digital signal processor (DSP) to process the signal demodulation and achieve the feedback control of the gyroscope. Therefore, the small change of capacitance in the micromachined gyroscope can be detected. A new demodulation algorithm of least mean square demodulation (LMSD) has been developed inside DSP. Simulation and measurement results show that LMSD can improve 29% of the noise performance compared with the typical multiplication method. In air pressure, a kind of vibration-wheel micromachined gyroscope has achieved a noise level of 0.0073 deg s⁻¹·Hz^−1/2 over the 100-Hz bandwidth by using this digital readout technology. Translated from J. Tsinghua Univ. (Sci. Tech.), 2004, 44(5) (in Chinese)     

基于刚度轴偏角预估机制的 MEMS 多环谐振陀螺全闭环控制方法

针对微机电系统多环谐振陀螺正交闭环回路存在控制误差问题,提出一种基于刚度轴偏角预估机制的多环陀螺全闭环控制方法。该方法通过对微机电系统多环谐振陀螺刚度轴偏角预估,实现正交闭环回路参数自动优化调整。同时,提出了基于刚度轴偏角预估机制的全数字化闭环控制方法,实现微机电系统多环谐振陀螺的驱动、检测、正交、模态匹配环路的全闭环控制。该方法可提升正交闭环回路信噪比,增强陀螺正交漂移的抑制能力,降低陀螺零偏输出,改善陀螺的零偏不稳定性。实验结果表明,采用本文提出的基于刚度轴偏角预估机制的全闭环控制方法后,微机电系统多环谐振陀螺的零偏输出由0.201°/s降低为0.021 3°/s,零偏不稳定性由39.42°/h降低为1.237°/h,分别降低了9.44倍和31.86倍,验证了该方法对提升微机电系统多环谐振陀螺仪性能的有效性。     

频域时空图像测速法的图像滤波器敏感性分析

时空图像测速法是以河流表面图像中测速线为分析区域、通过检测合成时空图像的纹理主方向计算得到一维时均流速的测量方法,具有空间分辨率高、实时性强的特点。在实际应用中纹理主方向的检测精度难免会受到水面紊流、倒影、耀光、障碍物、降雨等环境扰动的影响,导致测量出现粗大误差。频域滤波技术是一种抑制噪声的有效方法,能显著提高时空图像的纹理清晰度。但现有研究在滤波器参数的敏感性分析方面存在不足,使得该方法的适用性受限。对此通过在水文站搭建在线视频测流系统采集了不同条件的河流水面视频数据,分析了6种典型场景下时空图像的空域及频域特性,进而开展了频域扇形滤波器方向角、通带夹角及半径参数的敏感性分析实验。实验结果表明：采用提出的椭圆形积分区域检测方向角优于现有的单像素宽直线;当设置通带夹角为±5.3°且半径为R/2时,滤波器在上述场景下均能有效地滤除噪声干扰。使得时空图像纹理主方向的检测精度在正常场景下达到0.1°,在复杂含噪场景下控制在0.5°以内,表面流速测量的相对误差小于6.2%。     

巡飞弹空中自适应快速初始姿态估计

为了解决巡飞弹空中上电后在无参考姿态条件下的初始姿态确定问题,采用低成本磁力计、陀螺仪和加速度计(MARG)传感器设计姿态航向参考系统(AHRS),并提出了一种自适应参考矢量权重的快速初始姿态估计(AFCF)算法。首先,提出了三轴传感器使用前的快速误差校准方法;然后,采用快速互补滤波算法进行姿态估计,分析了其权重函数对于初始姿态估计及收敛性等的影响;接着,提出自适应参考矢量权重及自适应姿态估计方法;最后,利用高精度MTI(Milliren Technologies,Inc)传感器数据对算法进行了验证,并在低成本MARG姿态航向参考系统中对算法进行了实现,对比了改进算法及扩展卡尔曼滤波(EKF)算法的性能。实验结果与分析表明:动态条件下采用MTI传感器数据,改进算法能够在初始时刻收敛,比快速互补滤波(FCF)算法提前约4s;解算精度约为±0.6°,初始时刻精度明显优于FCF;硬件测试则表明改进算法的处理时间为0.062ms,仅为EKF算法的1/9,解算精度约为±1.3°,能够满足姿态测量过程快速收敛、高精度、实时性等要求。     

基于压缩感知理论的MEMS陀螺仪信号降噪研究

通过分析传统小波阈值滤波的局限性,将基于压缩感知的小波滤波方法应用于低精度MEMS(micro electro mechanicalsystem)陀螺仪信号降噪中,并与小波阈值滤波方法进行了实验对比,实验结果表明:基于压缩感知的小波滤波方法可以有效地去除MEMS陀螺仪输出信号中的噪声,并且在压缩比较大时基于压缩感知的滤波方法去噪效果优于小波阈值滤波方法,改善了低精度MEMS陀螺仪零偏稳定性,为工程中解决低精度MEMS陀螺仪降噪问题提供了新思路。     

A fast temperature rise identification method based on an adaptive particle filter

A fast temperature rise identification method for motorized spindle is presented based on an adaptive particle filter (APF). The advantage of this method is the ability to predict the entire temperature change in a short time. For example, to obtain the whole temperature variation curve of the measured points, it will take about 390 min. However, if APF method is used, the data of the first 24 min can be used to predict the entire temperature change. Temperature signals with multiple variables, long time delay, and nonlinear coupling characteristics as well as the uncertainty of actual measurement of noise in the process making measured temperature fluctuate widely. However, sampled data contain a lot of noise; hence, finding the real temperature range and predicting the future temperature changes using only few data are quite challenging tasks. For this reason, particle filter (PF) method was utilized to get the identification result. Then, the adaptive factor was introduced by adjusting state equations and observation noise variance. Results showed that APF can achieve better identification of temperature rise. Finally, the temperature rise test was verified on the motorized spindle, which utilized PF method and the APF method. Experimental results showed that APF can reduce the recognition time to about 24 min, while the PF takes about 38 min. The effectiveness of the method was verified by using experimental data, and the approach to obtain the minimal recognition time was also explained. In 390 min of measuring time, the root mean square error (RMSE) between estimated and measured temperature is 0.415 °C, and the error between the estimated and measured steady-state temperature is 0.01 °C. The results proved that this method can be used to identify motorized spindle temperature, with high speed and high accuracy.     

基于EMMAP的MEMS陀螺噪声估计与滤波方法研究

针对MEMS陀螺仪测量精度低、随机噪声具有不确定性和非线性的问题,提出一种基于最大期望算法(Expectation maximum,EM)和极大后验估计(Maximum a posterion,MAP)的无迹卡尔曼滤波(Unscented Kalman filter,UKF)——EMMAP-UKF的陀螺噪声估计与滤波方法。根据极大后验估计原理,构造出一种次优无偏MAP噪声统计估计模型,并在此基础上引入最大期望算法将噪声估计问题转换为数学期望极大化问题,实现对观测噪声方差的动态调整,最终实现陀螺仪随机漂移误差的估计与滤波处理。最后通过Allan方差对陀螺噪声滤波方法的性能进行评估,通过半实物仿真验证了本方法的有效性。     

干涉式光纤陀螺控制器的设计与仿真

光纤陀螺的研究与开发目前主要集中在工程应用技术方面.干涉式光纤陀螺(IFOG)的动态性能受控制系统设计的影响.现根据干涉式光纤陀螺数字闭环检测过程,建立了控制系统的动态模型,详细分析并设计了数字PID控制器和IIR滤波器.仿真结果显示,设计的控制器能够有效地提高干涉式光纤陀螺的输出带宽,改善系统的动态特性.     

Optimal estimation of the relevant information coming from a variable reluctance proximity sensor placed in a car undergoing performance tests

Today's automotive industry has a soaring interest in efficient, reliable and robust sensors able to make intelligent driving decisions that can save millions of lives every year. To that end, the sensors used in today's cars are being provided with microprocessors and application-specific integrated circuit technologies that incorporate a certain amount of intelligence into the sensors themselves. In this paper, an inverse square-root adaptive filtering algorithm for recursive least-squares estimation (QR-RLS) is used to improve the response of a wheel speed sensor placed in a car undergoing performance tests. Such an algorithm is used to carry out an optimal estimation of the relevant signal coming from the sensor, which is buried in a broad-band noise background where we have little knowledge of the noise characteristics. The results of the experiment are satisfactory, a significant improvement of 32.5 dB in the signal-to-noise ratio at the QR-RLS adaptive filter output was achieved. Also, in order to compare classical filtering techniques with optimal adaptive filtering techniques, the signal coming from the wheel speed sensor was also filtered by using a second-order lowpass digital Butterworth filter. The results of comparing the aforementioned filters show that the optimal adaptive filter is superior to the classical filter.     

基于带通Σ-Δ调制器的硅微机械陀螺力反馈闭环检测

为了提高硅微机械陀螺(SMG)的性能,研究了一种基于四阶机电结合带通Σ-Δ调制器(SDM)的硅微机械陀螺力反馈闭环检测方法。基于谐振器级联谐振前馈(CRFF)结构设计了该方法的仿真模型,并利用商用软件SD TOOLS计算了环路参数。采用MATLAB/SIMULINK对设计结果进行了行为级仿真,结果表明,1 Hz条件下环路的信噪比达到了109.2dB,符合设计预期。在此基础上,以现场可编程门阵列(FPGA)为数字处理核心搭建了硅微机械陀螺数字化测控电路并进行了性能测试。结果表明,采用带通SDM闭环检测技术和数字化闭环驱动技术后,硅微机械陀螺的Allan方差零偏不稳定性约为1.15(°)/h,角度随机游走约为7.74×10-2(°)/√h,且信噪比参数满足了设计目标。得到的结果证明了设计方法的正确性;显示提出的带通SDM力反馈闭环检测方法有助于提高SMG的性能,拓展其应用领域。     

Adaptive tuning piecewise cubic Hermite interpolation with Wiener filter in wavelet domain for scanning electron microscope images

Image processing is introduced to remove or reduce the noise and unwanted signal that deteriorate the quality of an image. Here, a single level two‐dimensional wavelet transform is applied to the image in order to obtain the wavelet transform sub‐band signal of an image. An estimation technique to predict the noise variance in an image is proposed, which is then fed into a Wiener filter to filter away the noise from the sub‐band of the image. The proposed filter is called adaptive tuning piecewise cubic Hermite interpolation with Wiener filter in the wavelet domain. The performance of this filter is compared with four existing filters: median filter, Gaussian smoothing filter, two level wavelet transform with Wiener filter and adaptive noise Wiener filter. Based on the results, the adaptive tuning piecewise cubic Hermite interpolation with Wiener filter in wavelet domain has better performance than the other four methods.     

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.     

A modified model reference adaptive control with application to MEMS gyroscope

Micro electro-mechanical systems (MEMS) are increasingly being used in measurement and control problems due to their small size, low cost, and low power consumption. The vibrating gyroscope is a MEMS device that will have a significant impact on stability control systems in the transportation industry. This paper investigates the application of a modified model reference adaptive control for MEMS gyroscope. Using this adaptive control algorithm, an estimation of the angular velocity and the damping and stiffness coefficients in real time is easily computable. Changing the conventional model reference input makes it feasible to utilize a low pass filter to remove unwanted oscillations caused by high adaptation gain. This new adaptive control technique enables quick compensation for large changes in the system dynamics, providing consistent estimation of gyroscope parameters including angular velocity and large robustness to parameter variations and external disturbances. The asymptotic stability of the mentioned adaptive controller is guaranteed using the Lyapunov direct method. Numerical simulation is presented to verify the effectiveness of the proposed control scheme.     

线阵CCD光谱采集系统的自适应滤波降噪

为了便于对微型线阵CCD光谱采集系统采集的光谱数据进行分析,需要对光谱数据采集过程中出现的噪声进行降噪处理,以提高光谱数据的信噪比。首先,根据线阵CCD参数指标,设计了一种硬件降温结构,并用它对线阵CCD进行降温去噪。接着,根据递归最小二乘自适应滤波算法对采集好的水样品光谱数据进行去噪处理,然后和未去噪的水样品数据对比。实验表明,硬件电路降温去噪能够衰减线阵CCD上的暗电流噪声,使用递归最小二乘自适应滤波方法能够有效消减光谱采集系统中光谱数据的噪声。     

MRT letter: Quantum noise removal and classification of breast mammogram images

Because of the limitations of the X‐ray hardware systems in mammogram machines, the quality of the breast mammogram images may undergo from poor resolution or low contrast. Quantum noise occurs in the mammogram images during acquisition due to low‐count X‐ray photons. In this work, an adaptive frost filter has been used to remove quantum noise. Local binary patterns have been extracted to classify breast mammograms into benign and malignant using different classifiers. Results show the superiority of the proposed algorithm in terms of sensitivity, specificity, and accuracy. Mammographic Institute Society Analysis database of mammography has been used for experimentation. Peak signal‐to‐noise ratio and structural similarity index measure are used to test the validity of adaptive frost filter. Experiment results show that proposed technique produces better results. Microsc. Res. Tech. 2012. © 2012 Wiley Periodicals, Inc.     

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%。     

硅微陀螺正交误差校正方案优化

本文意在寻求双质量硅微机械陀螺仪正交校正最优方案。首先介绍了带有正交校正和检测力反馈梳齿的双质量硅微机械陀螺结构,量化分析了正交误差对输出信号的影响并进行了仿真,结果显示解调相角变化为±2°,200(°)/s的正交误差等效输入角速率可引起15(°)/s的输出信号变化。然后,对目前3种比较主流的硅微机械陀螺仪正交校正方法(电荷注入法(CIM)、正交力校正法(QFCM)和正交耦合刚度校正法(QCSCM))进行了实验研究,从理论上证明了这3种方法的可行性。对未加入正交校正环节的陀螺进行了实验,结果显示其左、右质量块输出的正交误差信号峰峰值分别为150mV和300mV。针对两质量块正交误差不等的实际问题提出了质量块单独校正的方案。采用CIM、QFCM和QCSCM对校正前零偏及其稳定性分别为-4.589(°)/s和378(°)/h的陀螺进行了实验校正,结果显示3种方法均可有效消除检测通道中正交信号,3种方法的零偏及零偏稳定性结果分别为-8.361(°)/s和423(°)/h,2.419(°)/s和82(°)/h,1.751(°)/s和25(°)/h,证明了正交耦合刚度校正法为3种方法中的最优方案。     

基于传感器数据融合的倾斜角度测量方法研究

倾斜角度的测量精度直接决定了状态控制系统的工作效果。在单一传感器测量倾斜角度的研究基础上,探讨了传感器数据融合技术用于倾斜角度测量的方法。首先分析基于加速度计和陀螺仪测量倾斜角度的原理,并研究加速度计和陀螺仪测量结果的频率特性;然后根据加速度计和陀螺仪测量结果的频率特性选定互补滤波器作为数据融合的方法;最后选定量子粒子优化群(QPSO)算法作为互补滤波器的参数寻优方法,并对比量子粒子优化群算法和粒子群优化算法的参数寻优效果。实验结果表明,互补滤波器可以在广泛频域范围内准确测量倾斜角度值,并且量子粒子群优化算法相对于粒子群优化算法具有更好的参数寻优效果。     

LYAPUNOV TUNING OF THE LEAKY LMS ALGORITHM FOR SINGLE-SOURCE,SINGLE-POINT NOISE CANCELLATION

Least-mean square (LMS) algorithms, which are commonly used for adaptive feedforward noise cancellation, have performance issues related to insufficient excitation, non-stationary reference inputs, finite-precision arithmetic, quantisation noise and measurement noise. Such factors cause weight drift and potential instability in the conventional LMS algorithm. Here, we analyse the stability and performance of the leaky LMS algorithm, which is widely used to correct weight drift. A Lyapunov tuning method is developed to find an adaptive leakage parameter and adaptive step size that provide optimum performance and retain stability in the presence of measurement noise on the reference input of known variance. The method accounts for non-persistent excitation conditions and non-stationary reference inputs and requires no a priori knowledge of the reference input signal characteristics other than a lower bound on its magnitude or a minimum signal-to-noise ratio. The Lyapunov tuning method is demonstrated for three candidate adaptive leakage and step size parameter combinations, each of which is a function of the instantaneous measured reference input, measurement noise variance, and/or filter length. These candidates illustrate stability vs performance tradeoffs in the leaky LMS algorithm elicited through the Lyapunov tuning method. The performance of each candidate Lyapunov tuned algorithm is evaluated experimentally in a single source, single-point acoustic noise cancellation system.     

硅微陀螺仪高精度数字化相敏解调ASIC

为了抑制相敏解调引入的闪频(1/f)噪声,实现硅微陀螺仪的高精度片上数字化输出,设计了一种基于sigma-delta模数转换器的相敏解调ASIC。首先,提出了一种量化硅微陀螺仪驱动及检测载波信号的低闪频噪声数字化方案,建立了sigma-delta模数转换器的系统级分析模型,并利用Simulink完成了基于谐振器级联前馈(CRFF)结构的三阶sigmadelta模数转换器系统级设计。其次,研究了sigma-delta模数转换器的电路级实现方法,在Cadence IC平台上完成了包括低噪声开关电容积分器、加法器及1bit高速量化器等模块的晶体管级电路设计与验证,并采用AMS 0.35μm工艺进行了流片。实验表明:该sigma-delta模数转换器具有三阶噪声整形功能,在硅微陀螺仪的工作频率处(6.4kHz)量化噪声小于200nV/Hz1/2,等效精度位数为12。硅微陀螺仪数字化输出角度随机游走0.012(°)/h1/2,Allan方差零偏不稳定性为0.34(°)/h,零偏稳定性(1σ)为0.94(°)/h,满足高精度硅微陀螺仪的数字化精度需求,并提高了整表集成度。