基于IQPSO-EKF的多传感器融合姿态测量方法研究

为解决自动化竖井掘进设备的定位调姿精度对竖井、孔桩挖掘效率与质量的影响,提出了一种基于改进量子粒子群(IQPSO)-扩展卡尔曼滤波(EKF)的姿态测量算法,以提高微机电系统(MEMS)传感器测量精度。首先,对MEMS传感器数据进行了预处理(除噪、滤波、校准等);然后,参考现有飞行器的坐标系,建立了姿态解算模型,通过姿态角数学模型及运动学分析,构建了EFK状态方程,针对EKF方法参数估计不准确的问题,以分段混沌映射优化初始种群,引入平均位置最优值来避免陷入局部最优的IQPSO-EFK算法,优化EKF的系统、测量噪声的协方差参数;最后,对改进算法和三组姿态误差估计进行了对比实验。研究结果表明:对比三种典型目标函数,IQPSO-EFK相较于普通粒子群算法(QPSO-EFK)具有更强的寻优能力与收敛精度;对比三组旋转速度姿态测量误差,基于IQPSO-EKF算法的姿态测量方法在测量误差时比真实测量误差减少了约86.3%,比扩展卡尔曼滤波减少了约68.7%,比普通粒子群算法减少了约28.2%,证明该算法有效地提高了MEMS传感器测量精度。

基于EKF和UKF的随钻姿态解算方法研究

目的为解决煤层松软中随钻测量系统测量精度不高的问题。方法提出一种改进的无迹卡尔曼滤波(UKF)和扩展卡尔曼滤波(EKF),分别应用于钻具的姿态滤波算法中并作比较。该方法基于旋转坐标变换的四元数理论和陀螺测量原理,建立钻具姿态传感器数据的非线性观测方程和状态方程,以四元数将测量数据进行转换与更迭,最终消除惯性传感器数据中的误差。与EKF算法相比较,UKF算法利用了UT变换对非线性函数的概率密度分布进行近似,没有忽略高项阶,因此对于非线性分布的统计量有较好的计算精度。结果经仿真验证,UKF的各个滤波误差峰峰值以及标准差小于EKF。结论改进的UKF的滤波算法精度明显高于EKF滤波算法,更加有效地去除惯性传感器中的干扰噪声,有利于提高微机电系统(MEMS)惯性传感器的测量精度,进而提高钻进效率。

基于高阶EKF的锂电池SOC测算精度研究

锂电池荷电状态是电池管理系统运行的前提和核心任务,为能够准确跟踪测算电池SOC值,以18650-20R型锂电池为主要研究对象,建立二阶Thevenin等效电路模型,经脉冲特性实验对电路模型参数进行辨识,在恒流、脉冲放电及Fuds工况下验证模型的准确性,并在此基础上实现了利用一阶、二阶及高阶EKF算法对电池荷电状态的估计。最后通过Matlab仿真结果验证,高阶EKF在锂离子电池动静态SOC测算时均具有更高的测算精度。

基于AFEKF的锂离子电池SOC估算方法

针对利用扩展卡尔曼滤波算法估算锂电池荷电状态时,由于历史数据影响易产生累积误差的问题,提出了一种基于自适应渐消扩展卡尔曼的SOC估算方法。选用Thevenin等效模型并用递推最小二乘法进行电池参数辨识,通过将自适应渐消因子引入EKF算法中,抑制历史数据对当前状态估算的影响,完成锂电池SOC估算。结果表明:AFEKF算法在递推20次时可有效收敛,具有较好鲁棒性,估算SOC的平均误差为1.03%,误差均方根为1.21%,平均运行时间为1.476 s,可以较好地模拟电池的动静态特性。

基于Mahony-EKF算法的手臂运动姿态测量系统

针对传统姿态解算方法效率迟缓、精度低下及稳定性差等问题,提出一种基于Mahony和扩展卡尔曼(EKF)相融合的算法,并开发出一种新型人体手臂姿态测量系统。首先,通过STM32微处理器采集MEMS传感器测得的数据,借助Mahony滤波器解算加速度计、磁力计和陀螺仪的数据,以此得到初步姿态四元数。其次,将初步姿态四元数作为EKF量测值,依据非重力加速度调节量测噪声协方差矩阵。然后,根据陀螺仪测得的角速度信息建立EKF状态方程,通过EKF滤波更新状态,获取解算融合后的手臂姿态数据。最后,将数据发送到上位机,通过上位机软件实时监测姿态角数据,再构建三维模型实时还原手臂的运动状态。经实验验证,应用EKF算法矫正Mahony滤波解算出的姿态数据,不仅可以使误差减小到0.5°、消除超调量和降低噪声干扰,还能有效克服传统姿态解算方法中需要大量数据集和计算时间长问题,从而抑制了随机波动,提高姿态解算精度。

一种基于Madgwick-EKF融合算法的卫星姿态测量方法

针对低地球轨道卫星姿态测量时,传感器易受噪声干扰、陀螺仪漂移等问题,提出一种基于Madgwick扩展卡尔曼滤波合算法(EKF)的卫星姿态测量方法。该方法采用陀螺仪、加速度计、磁强计等多传感器数据进行融合,并结合Madgwick算法和EKF算法的优点,实现姿态测量。首先,通过Madgwick算法,利用多个传感器测量数据计算初始姿态。然后,基于初始姿态和实际测量数据,应用EKF算法进行数据融合和噪声滤除,以获得最终准确的姿态估计。实验结果表明:相较Madgwick算法,本算法在测量精度上提升了65.8%,且具有较高的鲁棒性,为低地球轨道卫星姿态测量提供了一种有效的方案。

基于EKF算法的纯电动汽车锂电池SOC与SOH联合估算

为提高对动力电池的荷电状态(state of charge, SOC)估算精度、动力电池的健康状态(state of health, SOH)对锂电池性能的影响,提出一种扩展卡尔曼滤波(extended kalman filtering, EKF)联合估算算法。根据现有的实验数据,分析锂电池特性,构建二阶RC等效电路模型,并进行参数辨识,搭建MATLAB仿真平台联合EKF算法进行SOC估算,将仿真结果与真实数据进行对比,结果表明,EKF联合估算SOC比EKF估算SOC误差精度约高1.2%,且抗干扰能力更强。

Time-varying parameters estimation with adaptive neural network EKF for missile-dual control system

In this paper, a filtering method is presented to estimate time-varying parameters of a missile dual control system with tail fins and reaction jets as control variables. In this method, the long-short-term memory(LSTM) neural network is nested into the extended Kalman filter(EKF) to modify the Kalman gain such that the filtering performance is improved in the presence of large model uncertainties. To avoid the unstable network output caused by the abrupt changes of system states,an adaptive correction factor is introduced to correct the network output online. In the process of training the network, a multi-gradient descent learning mode is proposed to better fit the internal state of the system, and a rolling training is used to implement an online prediction logic. Based on the Lyapunov second method, we discuss the stability of the system, the result shows that when the training error of neural network is sufficiently small, the system is asymptotically stable. With its application to the estimation of time-varying parameters of a missile dual control system, the LSTM-EKF shows better filtering performance than the EKF and adaptive EKF(AEKF) when there exist large uncertainties in the system model.

基于ISTSMC和改进EKFSMO的PMSM无传感器矢量控制

在采用内环矢量控制(Field orientation control,FOC)和外环经典比例积分(Proportional integral,PI)控制的永磁同步电机(Permanent magnet synchronous motor,PMSM)驱动系统中,容易出现外部扰动检测精度低、抖振、速度环超调大等不确定性缺陷。为了解决PI控制的抖振缺点,提高系统抗干扰性,提出了一种积分超螺旋滑模控制(Integral super twisting sliding mode controller,ISTSMC)。为了实现无位置传感器控制以及电机转速与位置的在线估计,采用基于扩展卡尔曼滤波(Extended Kalmanfilter,EKF)的滑模观测器,并且设计了一种分段正弦型函数代替传统的基于sgn函数的滑模观测器降低抖振现象。在Matlab/Simulink仿真下,分析了该系统在各种故障扰动、反转运行和负载扰动下的速度响应和检测精度能力。结果证明了所提系统的可行性以及优越的动态性能。

基于CEP的重力自适应并行EKF匹配算法

针对现有惯性/重力/重力梯度组合导航EKF匹配算法滤波状态方程存在模型误差以及惯性导航系统定位存在累积误差而造成的滤波失准乃至发散问题,提出一种基于CEP(Circular Error Probable,圆概率误差)的自适应并行EKF匹配算法.该算法首先通过自适应因子调节状态预测信息的权重,削弱预设动力学模型不准确产生的误差;同时利用惯性导航系统圆概率误差半径构建基于CEP的移动窗口分层模型,然后根据滤波量测值与窗口坡度等信息,对移动窗口范围进行约束;最后组建分层窗口并行滤波器,得到最优匹配结果.南海海域实验结果表明,基于CEP的自适应并行EKF匹配算法相较于传统EKF算法和自适应EKF算法的水下重力匹配导航定位精度分别提升了74.0%和49.8%.该算法能够在一定程度上克服惯性导航系统由于时间推移误差积累的缺陷,提高系统导航定位精度,增加匹配算法的鲁棒性.

基于EKF滤波的飞行器自适应姿态修正算法研究

扩展卡尔曼滤波(Extended Kalman Filter,EKF)算法对硬件资源有限飞行器姿态的估计,容易受非线性因素的影响,使得估计的结果存在较大误差.考虑到低成本小型飞行器的适用性,结合飞行过程中要求解算速度快、稳定性好、精度高、便于实现的应用特点,本文提出了一种基于EKF的自适应姿态修正算法.结合陀螺仪特性与上一次的修正值,对本次EKF滤波器输出的姿态角进行修正,并利用修正的姿态角更新四元数微分方程代入EKF滤波器进行误差反馈调节.通过实验验证,算法稳定性较好、能有效降低系统的噪声误差,静态实验情况下姿态角方差降低80%以上;动态实验下姿态角的误差降低了50%左右.对于低成本姿态估计的工程场景具有重要的参考价值.

引入PID反馈的SHAEKF算法估算电池SOC

电池荷电状态(SOC)的估算精度是电动汽车电池组的重要指标。为提升SOC估算精度,在融合Sage-Husa扩展卡尔曼滤波(SHEKF)算法与自适应扩展卡尔曼滤波(AEKF)算法的基础上,增加比例积分微分(PID)反馈环节,形成改进算法。采用粒子群优化(PSO)算法对二阶RC等效电路模型进行参数辨识;用开源电池数据集对模型和算法进行实验和分析。改进的SHAEKF算法在电池动态应力测试(DST)、北京动态应力测试(BJDST)和美国联邦城市驾驶(FUDS)等工况下的平均估计误差都在1%以内,与单纯的融合算法SHAEKF算法相比,最大误差可减小5%。

Recursive Filtering for Stochastic Systems With Filter-and-Forward Successive Relays

In this paper,the recursive filtering problem is considered for stochastic systems over filter-and-forward successive relay(FFSR)networks.An FFSR is located between the sensor and the remote filter to forward the measurement.In the successive relay,two cooperative relay nodes are adopted to forward the signals alternatively,thereby existing switching characteristics and inter-relay interferences(IRI).Since the filter-and-forward scheme is employed,the signal received by the relay is retransmitted after it passes through a linear filter.The objective of the paper is to concurrently design optimal recursive filters for FFSR and stochastic systems against switching characteristics and IRI of relays.First,a uniform measurement model is proposed by analyzing the transmission mechanism of FFSR.Then,novel filter structures with switching parameters are constructed for both FFSR and stochastic systems.With the help of the inductive method,filtering error covariances are presented in the form of coupled difference equations.Next,the desired filter gain matrices are further obtained by minimizing the trace of filtering error covariances.Moreover,the stability performance of the filtering algorithm is analyzed where the uniform bound is guaranteed on the filtering error covariance.Finally,the effectiveness of the proposed filtering method over FFSR is verified by a three-order resistance-inductance-capacitance circuit system.

Bayesian Filtering for High-Dimensional State-Space Models With State Partition and Error Compensation

In the era of exponential growth of data availability,the architecture of systems has a trend toward high dimensionality,and directly exploiting holistic information for state inference is not always computationally affordable.This paper proposes a novel Bayesian filtering algorithm that considers algorithmic computational cost and estimation accuracy for high-dimensional linear systems.The high-dimensional state vector is divided into several blocks to save computation resources by avoiding the calculation of error covariance with immense dimensions.After that,two sequential states are estimated simultaneously by introducing an auxiliary variable in the new probability space,mitigating the performance degradation caused by state segmentation.Moreover,the computational cost and error covariance of the proposed algorithm are analyzed analytically to show its distinct features compared with several existing methods.Simulation results illustrate that the proposed Bayesian filtering can maintain a higher estimation accuracy with reasonable computational cost when applied to high-dimensional linear systems.

Dynamic Event-Triggered Quadratic Nonfragile Filtering for Non-Gaussian Systems:Tackling Multiplicative Noises and Missing Measurements

This paper focuses on the quadratic nonfragile filtering problem for linear non-Gaussian systems under multiplicative noises,multiple missing measurements as well as the dynamic event-triggered transmission scheme.The multiple missing measurements are characterized through random variables that obey some given probability distributions,and thresholds of the dynamic event-triggered scheme can be adjusted dynamically via an auxiliary variable.Our attention is concentrated on designing a dynamic event-triggered quadratic nonfragile filter in the well-known minimum-variance sense.To this end,the original system is first augmented by stacking its state/measurement vectors together with second-order Kronecker powers,thus the original design issue is reformulated as that of the augmented system.Subsequently,we analyze statistical properties of augmented noises as well as high-order moments of certain random parameters.With the aid of two well-defined matrix difference equations,we not only obtain upper bounds on filtering error covariances,but also minimize those bounds via carefully designing gain parameters.Finally,an example is presented to explain the effectiveness of this newly established quadratic filtering algorithm.

3D robust anisotropic diffusion filtering algorithm for sparse view neutron computed tomography 3D image reconstruction

The most critical part of a neutron computed tomography(NCT) system is the image processing algorithm,which directly affects the quality and speed of the reconstructed images.Various types of noise in the system can degrade the quality of the reconstructed images.Therefore,to improve the quality of the reconstructed images of NCT systems,efficient image processing algorithms must be used.The anisotropic diffusion filtering(ADF) algorithm can not only effectively suppress the noise in the projection data,but also preserve the image edge structure information by reducing the diffusion at the image edges.Therefore,we propose the application of the ADF algorithm for NCT image reconstruction.To compare the performance of different algorithms in NCT systems,we reconstructed images using the ordered subset simultaneous algebraic reconstruction technique(OS-SART) algorithm with different regular terms as image processing algorithms.In the iterative reconstruction,we selected two image processing algorithms,the Total Variation and split Bregman solved total variation algorithms,for comparison with the performance of the ADF algorithm.Additionally,the filtered back-projection algorithm was used for comparison with an iterative algorithm.By reconstructing the projection data of the numerical and clock models,we compared and analyzed the effects of each algorithm applied in the NCT system.Based on the reconstruction results,OS-SART-ADF outperformed the other algorithms in terms of denoising,preserving the edge structure,and suppressing artifacts.For example,when the 3D Shepp–Logan was reconstructed at 25 views,the root mean square error of OS-SART-ADF was the smallest among the four iterative algorithms,at only 0.0292.The universal quality index,mean structural similarity,and correlation coefficient of the reconstructed image were the largest among all algorithms,with values of 0.9877,0.9878,and 0.9887,respectively.

Electrically controllable spin filtering in zigzag phosphorene nanoribbon based normal–antiferromagnet–normal junctions

We investigated the electric controllable spin-filtering effect in a zigzag phosphorene nanoribbon(ZPNR) based normal–antiferromagnet–normal junction. Two ferromagnets are closely coupled to the edges of the nanoribbon and form the edge-to-edge antiferromagnetism. Under an in-plane electric field, the two degenerate edge bands of the edge-to-edge antiferromagnet split into four spin-polarized sub-bands and a 100% spin-polarized current can be easily induced with the maximal conductance 2e~2/h. The spin polarization changes with the strength of the electric field and the exchange field,and changes sign at opposite electric fields. The spin-polarized current switches from one edge to the other by reversing the direction of the electric field. The edge current can also be controlled spatially by changing the electric potential of the scattering region. The manipulation of edge current is useful in spin-transfer-torque magnetic random-access memory and provides a practical way to develop controllable spintronic devices.

A Novel Clutter Suppression Algorithm for Low-Slow-Small Targets Detecting Based on Sparse Adaptive Filtering

Passive detection of low-slow-small(LSS)targets is easily interfered by direct signal and multipath clutter,and the traditional clutter suppression method has the contradiction between step size and convergence rate.In this paper,a frequency domain clutter suppression algorithm based on sparse adaptive filtering is proposed.The pulse compression operation between the error signal and the input reference signal is added to the cost function as a sparsity constraint,and the criterion for filter weight updating is improved to obtain a purer echo signal.At the same time,the step size and penalty factor are brought into the adaptive iteration process,and the input data is used to drive the adaptive changes of parameters such as step size.The proposed algorithm has a small amount of calculation,which improves the robustness to parameters such as step size,reduces the weight error of the filter and has a good clutter suppression performance.

Adaptive H_(∞)Filtering Algorithm for Train Positioning Based on Prior Combination Constraints

To solve the problem of data fusion for prior information such as track information and train status in train positioning,an adaptive H∞filtering algorithm with combination constraint is proposed,which fuses prior information with other sensor information in the form of constraints.Firstly,the train precise track constraint method of the train is proposed,and the plane position constraint and train motion state constraints are analysed.A model for combining prior information with constraints is established.Then an adaptive H∞filter with combination constraints is derived based on the adaptive adjustment method of the robustness factor.Finally,the positioning effect of the proposed algorithm is simulated and analysed under the conditions of a straight track and a curved track.The results show that the positioning accuracy of the algorithm with constrained filtering is significantly better than that of the algorithm without constrained filtering and that the algorithm with constrained filtering can achieve better performance when combined with track and condition information,which can significantly reduce the train positioning error.The effectiveness of the proposed algorithm is verified.

基于EKF的GMAW实时焊缝跟踪研究

熔化极气体保护电弧焊(gas metal arc welding,GMAW)焊接过程存在惯性大、时滞大等非线性特征以及不确定的干扰因素。为了提高焊接质量,文章提出了一种基于扩展卡尔曼滤波器(extended kalman filter,EKF)算法的焊缝实时跟踪技术,采用霍尔电流传感器来捕捉实时焊接电流。为了实现焊枪摆动中心始终对准焊缝中心,文章通过有限长单位冲激响应滤波器(finite impulse response,FIR)对采集的电流进行滤波,并建立提取焊枪高度和水平偏差的数学模型,采用EKF实现控制焊枪。通过在有垂直和水平方向偏差的焊缝上进行实验验证,结果跟踪精度可达到±0.25 mm,所以该方法可满足机器人实时跟踪的要求。