基于小波支持向量机的陀螺随机漂移预测模型研究

通过小波变换抑制各种干扰噪声,预处理后的陀螺漂移数据采用支持向量机的方法建立陀螺漂移预测模型。试验得到的陀螺漂移数据对提出的模型进行验证。结果表明,相对于独立的支持向量机模型（SVM）和径向基神经网络模型（RBF）,提出模型得到的陀螺随机漂移预测精度更高。     

基于小波-强跟踪滤波的陀螺漂移在线预测

为进一步提高陀螺漂移预测精度,根据陀螺一次项漂移系数非平稳时间序列的特点,针对其数据的突变和趋势相较强的问题,提出一种基于小波和多重次优渐消因子强跟踪滤波相结合的非平稳时间序列在线预测方法,并将其应用于陀螺一次项漂移系数预测。实验结果表明,该方法能有效改善数据突变和较强趋势项所带来的状态估计不准、进而造成预测不准的问题,提高了预测精度。     

基于小波-强跟踪滤波的陀螺漂移在线预测

为进一步提高陀螺漂移预测精度,根据陀螺一次项漂移系数非平稳时间序列的特点,针对其数据的突变和趋势相较强的问题,提出一种基于小波和多重次优渐消因子强跟踪滤波相结合的非平稳时间序列在线预测方法,并将其应用于陀螺一次项漂移系数预测。实验结果表明,该方法能有效改善数据突变和较强趋势项所带来的状态估计不准、进而造成预测不准的问题,提高了预测精度。     

基于均值漂移和粒子滤波相结合的水下运动目标跟踪

泳池是一个变化较大的动态场景,会受到气泡、光照强度和水波等因素的干扰,针对泳池环境的复杂性特点,本文提出一种基于均值漂移和粒子滤波相结合的水下运动目标跟踪算法。首先,结合Mean Shift算法中的核函数原理和目标模型,以RGB颜色直方图为核心建立水下运动目标模型,然后在粒子滤波跟踪水下目标的过程中,利用Mean Shift算法对粒子进行收敛,使粒子的分布更加接近目标的真实位置。仿真试验结果表明,本文提出的算法能够克服水波、阴影、气泡、遮挡等因素的干扰,实现了水下复杂背景下的实时稳定的目标跟踪。     

新型陀螺漂移性能测量的研究

针对以往陀螺漂移检测仪器的缺点,提出了一种新型的采用光学原理测试陀螺漂移装置的设计方法,对其工作原理及测试方法进行了较详细的描述;在此基础上与传统漂移测试仪器的优缺点进行了类比,并对实际测试的验证结果进行了总结分析,提出了进一步提高测量精度及完善的设想,为研究漂移性能的检测测量提供了理论指导及实践参考。     

一种改进的粒子滤波跟踪算法的应用研究

粒子滤波跟踪算法计算量大且存在严重的粒子退化现象,因此提出了将均值漂移算法嵌入到粒子滤波的跟踪框架中的算法,该算法可以改善粒子滤波的退化现象,大大减少了算法的运行时间,同时克服了均值漂移算法容易陷入局部最大且无法恢复的缺点。监控系统应用的仿真实验结果表明,该方法具有较强的实时性和鲁棒性。     

基于改进小波阈值的激光陀螺漂移信号降噪

针对固定阈值法对激光陀螺漂移信号去除噪声会出现"过扼杀"小波系数的现象,提出一种称为E-median的小波阈值.信号进行平稳小波变换,计算各尺度高频小波系数的E-median阈值,采用软阈值法修正高频小波系数,通过平稳小波逆变换重构信号.对激光陀螺漂移仿真信号和实测信号去除噪声,该阈值均能保留原信号的特征并有效去除噪声...     

分层近似粒子滤波及其在陀螺寻北中的应用

针对粒子滤波的粒子退化和匮乏等问题,在粒子滤波和网格近似的基础上,提出了一种采用分层近似策略的粒子滤波改进算法.改进算法利用高斯分布对后验概率密度进行近似,并在连续分布的后验概率密度上进行分层近似,从而获得更具效率的粒子,提高了粒子滤波的精度.光纤陀螺的寻北精度主要取决于光纤陀螺自身的性能以及所采用的寻北方案,将采用分层近似策略的改进粒子滤波算法应用于光纤陀螺的寻北方案中,能够有效地解决光纤陀螺寻北中的非线性状态估计问题,提高光纤陀螺的寻北精度.     

秩粒子滤波

将秩滤波与粒子滤波相结合,提出秩粒子滤波。它首先由秩滤波方法得到重要性密度函数,然后再采用粒子滤波方法进行滤波。由于秩滤波产生的重要性密度函数包含了最新的观测信息,与真实状态概率密度的支集重叠部分更大,所以,秩粒子滤波比无迹粒子滤波和粒子滤波具有更高的精度,且计算简单,便于工程应用。大量仿真结果验证了这一结论。     

粒子滤波在光纤陀螺四位置寻北中的应用研究

为了减小噪声对光纤陀螺(FOG)寻北的影响,提高寻北精度,提出了将粒子滤波这种非线性滤波方法应用于光纤陀螺四位置寻北的方案。根据四位置寻北模型建立光纤陀螺寻北系统的非线性状态空间模型,将基于系统重采样和在重采样后引入马尔可夫链蒙特卡罗(Markov chain monte carlo,MCMC)移动的粒子滤波器分别应用于光纤陀螺寻北系统的非线性滤波。使用一个零偏稳定性为0.05°/h的闭环光纤陀螺进行实验,实验结果表明,这2种粒子滤波器均能够有效地提高光纤陀螺的寻北精度。基于系统重采样的粒子滤波器有随时间发散的趋势,这是由于在选定粒子数目较少的情况下,重采样导致粒子多样性丧失的结果;而在重采样后引入MCMC移动的粒子滤波器有很好的收敛性能。重复实验结果表明,使用基于MCMC移动的粒子滤波器可以达到更好的寻北性能。     

基于改进EMD的微机械陀螺随机误差建模方法

为了降低微机械(MEMS)陀螺仪的随机误差,提出一种将改进的经验模态分解法(EMD)与传统建模滤波方法相结合的新方法对随机误差进行处理。首先采用传统EMD算法将信号分解为有限个本征模态函数(IMF),并根据皮尔逊相关系数准则和噪声统计特性提出一种筛选机制,将IMF分为噪声IMFs、混叠IMFs和信号IMFs 3类;其次,对混叠IMFs进行时间序列建模,建模完成后进行卡尔曼滤波拟合;最后,将建模滤波后的混叠IMFs与信号IMFs进行重构,得到最终去噪信号。实验分析结果表明,本文方法在抑制随机误差的效果上有明显的优势,极大地改善了信号的质量,提高了惯导的解算精度。     

小型动调陀螺随机误差建模与滤波方法研究

为了抑制动力调谐陀螺的随机漂移，采取时间序列分析的方法，分析了φ35小型动力调谐陀螺仪输出数据的平稳性，建立了其随机漂移的自回归求和滑动平均（autoregressive integrated moving average，ARIMA）模型。以所建模型作为状态方程、实际测量数据作为量测值，设计了卡尔曼滤波器，并应用卡尔曼滤波器对实际测量的动力调谐陀螺输出数据进行了滤波，处理后陀螺随机漂移仅为原数据的46．7％。结果表明，滤波方法能有效地抑制陀螺的随机漂移，同时也验证了所建模型的正确性和有效性。此方法也可应用于其他类型陀螺的输出数据处理。     

EMD- and SVM-based temperature drift modeling and compensation for a dynamically tuned gyroscope (DTG)

In this paper, support vector machine (SVM) is described and applied in the temperature drift modeling and compensation to reduce the influence of temperature variation on the output of dynamically tuned gyroscope (DTG) and to enhance its precision. To improve the modeling capability, empirical mode decomposition (EMD) is introduced into the SVM model to eliminate any impactive noises. The real temperature drift data set from the long-term measurement system of a certain DTG is employed to validate the effectiveness of the proposed combination model. The modeling and compensation results indicate that the proposed EMD-SVM model outperforms the neural network (NN) and single SVM models, and is feasible and effective in temperature drift modeling and compensation of the DTG.     

基于卡尔曼滤波的MEMS陀螺仪漂移补偿

为解决MEMS陀螺仪在测量过程中容易产生漂移的问题,将卡尔曼算法应用于陀螺仪的漂移补偿中.分析了陀螺仪误差源,建立了陀螺仪误差模型,提出了用卡尔曼滤波算法处理陀螺仪零点随机误差的方法,通过运用Allan方差分析法评价了卡尔曼滤波效果;最后,在转速测试平台上进行了陀螺仪测量试验.研究结果表明,通过建立误差模型和采用卡尔曼滤波算法能有效减小陀螺仪测量过程中的漂移.     

Cascaded Kalman and particle filters for photogrammetry based gyroscope drift and robot attitude estimation

Based on a cascaded Kalman–Particle Filtering, gyroscope drift and robot attitude estimation method is proposed in this paper. Due to noisy and erroneous measurements of MEMS gyroscope, it is combined with Photogrammetry based vision navigation scenario. Quaternions kinematics and robot angular velocity dynamics with augmented drift dynamics of gyroscope are employed as system state space model. Nonlinear attitude kinematics, drift and robot angular movement dynamics each in 3 dimensions result in a nonlinear high dimensional system. To reduce the complexity, we propose a decomposition of system to cascaded subsystems and then design separate cascaded observers. This design leads to an easier tuning and more precise debugging from the perspective of programming and such a setting is well suited for a cooperative modular system with noticeably reduced computation time. Kalman Filtering (KF) is employed for the linear and Gaussian subsystem consisting of angular velocity and drift dynamics together with gyroscope measurement. The estimated angular velocity is utilized as input of the second Particle Filtering (PF) based observer in two scenarios of stochastic and deterministic inputs. Simulation results are provided to show the efficiency of the proposed method. Moreover, the experimental results based on data from a 3D MEMS IMU and a 3D camera system are used to demonstrate the efficiency of the method.     

基于最小分辨率的MEMS陀螺漂移抑制方法研究

针对油气井MEMS陀螺定向测斜仪存在的漂移大,精度较低的问题,本文从实际工程应用角度出发,在研究MEMS陀螺仪输出信号漂移特性的基础上,提出了基于最小分辨率的MEMS陀螺漂移抑制方法,利用输出采样的差分值,从MEMS陀螺输出中将传感信号和漂移信号分离,同时通过静态测点对漂移进行估计.实验表明,该方法大幅提高了仪器测量精度,使定向精度提高50％以上,同时该方法为其它类似传感器漂移抑制提供了一种有益的参考.     

Digital signal processing for a micromachined vibratory gyroscope based on a three dimensional adaptive filter demodulator

This paper reports a digital controller based on a three dimensional adaptive filter demodulator (AFD) for micromachined vibratory gyroscopes with the goal of eliminating common-mode noise and reducing hardware resources. The least mean square (LMS) adaptive filter, which has advantages of fast convergence speed, lower noise and fewer occupied hardware resources, is adopted to demodulate the vibration velocity of the gyroscope and detect its phase shift. A three dimensional AFD is proposed to eliminate the common-mode noise and quadrature coupling induced by the initial capacitance mismatch. Simulation and experimental results have verified the effectiveness of this method. The measurement results of the digital controlled gyroscope show a zero bias drift of 24.6 °/h and a nonlinearity of 0.1% with the measurement range of ±200°/s.     

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.     

Temperature drift modeling and compensation of RLG based on PSO tuning SVM

Precision and generalization ability are the two main requirements for modeling the temperature drift of a Ring Laser Gyroscope (RLG). Traditional methods such as the least square fitting and artificial neural network cannot achieve the optimal performance for both aspects. To solve this problem, a novel modeling method based on particle swarm optimization (PSO) tuning support vector machine (SVM) with multiple temperature variables input is proposed. First, the temperature drift data for modeling is preprocessed by adaptive forward linear prediction (FLP) filter. Then, the SVM method is employed to construct the drift model and guarantee the generalization ability. And the PSO algorithm is used to tune the parameters of SVM and improve the precision of established model. The results of experiment validate the superiority of the proposed method in both aspects. The method has been practically applied to a high precision RLG position and orientation system.     

石英MEMS陀螺漂移周期性误差补偿研究

为提高基于石英MEMS陀螺仪的微小型惯性导航系统的性能,本文对应用于MIMU的石英MEMS陀螺仪零点漂移中周期性误差补偿方法的原理和特点进行分析研究,得到一种新的补偿方法.该方法先利用数据平滑前的数据确定误差折算公式的系数,再以折算公式补偿数据平滑后得到的低频数据.从实验结果来看,这种方法在保证实时性要求的前提下,能避开数据平滑过程对周期性误差频率稳定性的影响,提高补偿精度,是一种有效的石英系列MEMS陀螺仪漂移实时补偿方法.