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。

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。

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EKF融合的室内定位技术研究

针对单一室内定位技术的局限性,提出了一种基于扩展卡尔曼滤波(EKF)融合WiFi和行人航位推算(PDR)的定位方法。不同于传统WiFi指纹定位,本文基于随机森林(RF)模型建立多个基分类器,取投票结果的众数作为输出结果;通过采集手机内置传感器数据解算行人的步频、步长,并基于四元数进行航向估计。本文在EKF融合定位时,根据状态模型得到状态的预测值,RF模型输出观测值,根据观测值更新状态估计,推算下一时刻位置。试验表明,本文研究的融合算法的定位精度可达到1.26 m,比单一定位算法定位精度提高了1.07 m。

改进强跟踪EKF算法在MEMS姿态解算中的研究

本文针对四旋翼姿态解算,提出了一种噪声自适应强跟踪扩展卡尔曼滤波算法(ASTEKF)。当机体从平稳状态向机动状态过渡时,由于量测噪声影响会导致算法估计不准确,因此本文首先证明不同时刻新息序列方差满足正交性原理,正交性原理表明,量测噪声对观测值的准确性影响很大;其次,引入Sage-Husa噪声自适应估计器较准确估计系统量测噪声均值和方差,使观测值更准确;最后,通过满足正交性原理条件公式计算次优渐消因子,将次优渐消因子引入协方差一步预测运算式中,得到强跟踪滤波器。次优渐消因子的引入使得一步预测协方差矩阵增大,即增大强跟踪扩展卡尔曼滤波器增益,使系统增加对观测值权重,得到更准确的状态估计值。离线仿真实验和在线实物实验结果表明了所设计算法的有效性。

Unscented extended Kalman filter for target tracking

A new method of unscented extended Kalman filter （UEKF） for nonlinear system is presented. This new method is a combination of the unscented transformation and the extended Kalman filter （EKF）. The extended Kalman filter is similar to that in a conventional EKF. However, in every running step of the EKF the unscented transformation is running, the deterministic sample is caught by unscented transformation, then posterior mean of non- lineadty is caught by propagating, but the posterior covariance of nonlinearity is caught by linearizing. The accuracy of new method is a little better than that of the unscented Kalman filter （UKF）, however, the computational time of the UEKF is much less than that of the UKF.

Airship aerodynamic model estimation using unscented Kalman filter

An airship model is made-up of aerostatic,aerodynamic,dynamic,and propulsive forces and torques.Besides others,the computation of aerodynamic forces and torques is difficult.Usually,wind tunnel experimentation and potential flow theory are used for their calculations.However,the limitations of these methods pose difficulties in their accurate calculation.In this work,an online estimation scheme based on unscented Kalman filter(UKF)is proposed for their calculation.The proposed method introduces six auxiliary states for the complete aerodynamic model.UKF uses an extended model and provides an estimate of a complete state vector along with auxiliary states.The proposed method uses the minimum auxiliary state variables for the approximation of the complete aerodynamic model that makes it computationally less intensive.UKF estimation performance is evaluated by developing a nonlinear simulation environment for University of Engineering and Technology,Taxila(UETT)airship.Estimator performance is validated by performing the error analysis based on estimation error and 2-σ uncertainty bound.For the same problem,the extended Kalman filter(EKF)is also implemented and its results are compared with UKF.The simulation results show that UKF successfully estimates the forces and torques due to the aerodynamic model with small estimation error and the comparative analysis with EKF shows that UKF improves the estimation results and also it is more suitable for the under-consideration problem.

Extended Kalman filtering-based channel estimation for space-time coded MIMO-OFDM systems

A space-time coded multiple-input multiple-output orthogonal frequency-division multiplexing （MIMO-OFDM） system is considered as a solution to the future wideband wireless communication system. This paper proposes an extended Kalman filtering-based （EKF-based） channel estimation method for space-time coded MIMO-OFDM systems. The proposed method can exploit pilot symbols and an extended Kalman filter to estimate channel without any prior knowledge of channel statistics. In comparison with the least square （LS） and the least mean square （LMS） methods, the EKF-based approach has a better performance in theory. Computer simulations demonstrate the proposed method outperforms the LS and LMS methods. Therefore it can offer draznatic system performance improvement at a modest cost of computational complexity.

Attitude estimation method based on extended Kalman filter algorithm with 22 dimensional state vector for low-cost agricultural UAV

To overcome the shortcomings of traditional artificial spraying pesticides and make more efficient prevention of diseases and pests,a coaxial sixteen-rotor unmanned aerial vehicle(UAV)with pesticide spraying system is designed.The coaxial sixteen-rotor UAV’s basic structure and attitude estimation method are explained.The whole system weights 25 kg,cruising speed can reach 15 m/s,and the flight time is more than 20 min.When the UAV takes large load,the traditional extended Kalman filter(EKF)attitude estimation method can not meet the work requirements under the condition of strong vibration,the attitude measure accuracy is poor and the attitude angle divergence is easily caused.Hence an attitude estimation method based on EKF algorithm with 22 dimensional state vector is proposed which can solve these problems.The UAV system consists of STM32F429 as controller,integrating following measure sensors:accelerometer and gyroscope MPU6000,magnetometer LSM303D,GPS NEO-M8N and barometer.The attitude unit quaternion,velocity,position,earth magnetic field,biases error of gyroscope,accelerometer and magnetometer are introduced as the inertial navigation systems(INS)state vector,while magnetometer,global positioning system(GPS)and barometer are introduced as observation vector,thus making the estimate of the navigation information more accurate.The control strategy of coaxial sixteen-rotor UAV is based on the control method of combining active disturbance rejection control(ADRC)and proportion integral derivative(PID)control.Actual flight data are used to verify the algorithm,and the static experiment shows that the precision of roll angle and pitch angle of the algorithm are±0.1°,the precision of yaw angle is±0.2°.The attitude angle output of MTi sensor is used as reference.The dynamic experiment shows that the accuracy of attitude estimated by EKF algorithm is quite similar to that of MTi’s output,moreover,the algorithm has good real-time performance which meets the need of high maneuverability of agricultural UAV.

基于测量噪声方差自适应的EKF无传感器控制

提出一种测量噪声方差自适应的扩展卡尔曼滤波(EKF)算法。该算法分别对当前时刻和前一时刻的观测值施加两次EKF算法,将速度与位置观测值的误差百分比的加权和作为测量噪声方差的加权系数,实现测量噪声方差的自适应调整,提高转速和位置的观测精度和控制性能。通过实验平台验证了该算法的可行性。

Maneuvering Target Tracking Algorithm Based on Muti-paramter Sequential Extended Kalman Filter

Based on the information theory,the performance of maneuvering target tracking can be improved by increasing the input information( observation vector).In this paper,the estimations of radial acceleration and radial velocity obtained in the signal processing are introduced into the measurement vector by coordinate transformation.In order to solve the problem of high nonlinearity of the radial acceleration,radial velocity and the state vector,a new algorithm of multi-parameter sequential extended Kalman filter( MSEKF) is proposed.The tracking performance of this algorithm is tested and compared with the other tracking algorithms.It is shown that the proposed algorithm outperforms these algorithms in strong and weak maneuvering environments.

基于改进EKF的激光和视觉SLAM融合算法

角点特征在机器人同步定位与建图(Simultaneous Localization and Mapping,SLAM)系统中具有关键性的作用。然而,由于环境差异、机器人运动距离和传感器的影响,导致现有测量方法的角点估计误差较大。本文在原有使用扩展卡尔曼滤波(Extended Kalman Filter,EKF)融合激光和视觉SLAM数据的基础上,引入多新息理论,提出了多新息改进EKF融合激光和视觉SLAM数据算法。由于多新息理论能有效利用历史时刻的数据,使系统在原先只使用当前时刻数据的情况下,扩展为能够利用之前多个时刻的有效数据。因此,利用多新息理论改进EKF,可以充分利用之前时刻由角特征和垂线特征融合成的角点结果,从而提升角点估计精度和建图结果。实验结果表明,在室内坏境中,本文方法在迭代次数20次和100次时平均误差分别为0.0268和0.0109,相较于未改进EKF方法,角点估计的精度平均提升了33.9%。

Kalman Filters versus Neural Networks in Battery State-of-Charge Estimation: A Comparative Study

Battery management systems (BMS) must estimate the state-of-charge (SOC) of the battery accurately to prolong its lifetime and ensure a reliable operation. Since batteries have a wide range of applications, the SOC estimation requirements and methods vary from an application to another. This paper compares two SOC estimation methods, namely extended Kalman filters (EKF) and artificial neural networks (ANN). EKF is a nonlinear optimal estimator that is used to estimate the inner state of a nonlinear dynamic system using a state-space model. On the other hand, ANN is a mathematical model that consists of interconnected artificial neurons inspired by biological neural networks and is used to predict the output of a dynamic system based on some historical data of that system. A pulse-discharge test was performed on a commercial lithium-ion (Li-ion) battery cell in order to collect data to evaluate those methods. Results are presented and compared.

室内外混合环境下基于IMM-EKF的AGV连续定位方法研究

针对室内外混合环境下自动导引车(AGV)连续定位中存在多模型不匹配竞争、定位精度差的问题,提出一种基于交互式多模型—扩展卡尔曼滤波(IMM-EKF)的AGV室内外连续定位算法。针对AGV连续定位存在定位精度差的问题,提出采用平行扩展卡尔曼滤波器分别实现激光雷达(LiDAR)/里程计(ODOM)、全球导航卫星系统(GNSS)/ODOM的融合滤波定位。针对AGV连续定位存在多模型不匹配竞争问题,提出通过模型的似然概率分别计算LiDAR和GNSS模型概率,并根据模型概率对定位结果进行加权融合,从而计算AGV的最优位姿估计。AGV连续定位实验结果表明:本文提出融合LiDAR/GNSS/ODOM的IMM-EKF连续定位滤波算法,极大地提高了室内外连续定位精度、并有效抑制模型间的不匹配竞争关系,实现AGV的实时全局精准定位。

基于FFRLS+EKF的特定工况下铅炭电池SOC估计

提出一种快速、高精度估计铅炭电池荷电状态(SOC)的方法,并在特定工况下进行验证。通过建立等效电路模型,应用MATLAB仿真出SOC曲线,对比遗忘因子递推最小二乘(FFRLS)法+扩展卡尔曼滤波(EKF)估计的SOC与实际SOC曲线的误差,验证算法的精确性和可靠性。在恒流间歇放电特定工况下,使用所提算法估计铅炭电池的SOC,与实际SOC的最大误差不超过0.9%。

Application of Unscented Kalman Filter in Satellite Orbit Simulation

A new estimate method is proposed, which takes advantage of the unscented transform method, thus the true mean and covariance are approximated more accurately. The new method can be applied to non-linear systems without the linearization process necessary for the EKF, and it does not demand a Gaussian distribution of noise and what’s more, its ease of implementation and more accurate estimation features enables it to demonstrate its good performance in the experiment of satellite orbit simulation. Numerical experiments show that the application of the unscented Kalman filter is more effective than the EKF.

基于改进EKF的IMU动态误差抑制

惯性测量单元(Inertial measurement unit,IMU)三轴欧拉角的解算数据精度和抗干扰性能常受到系统高频噪音以及震动干扰的影响。基于此问题,本文提出一种适合嵌入式系统的低计算量、实时性好、低成本的动态误差抑制方法。该方法通过在扩展卡尔曼滤波器(Extended Kalman filter,EKF)算法前端引入一种无限脉冲响应滤波器(Infinite impulse response⁃extended Kalman filter,IIR⁃EKF),借助于二阶巴特沃斯低通滤波器(Butterworth filter,BF)对数据进行预处理来帮助EKF抑制高频或强干扰。IIR⁃EKF算法在STM32H743微控制器中实现,经过几种实验对比验证,结果表明:在EKF单独作用时,其数据方差较大,遇到震动干扰时,瞬时值误差较大;在无迹卡尔曼滤波(Unscented Kalman filter,UKF)单独作用时,虽然其并不依赖初始噪音参数,其数据方差比EKF小,但还不足以满足要求;在加入BF后,数据方差明显减小,瞬时误差被大幅抑制,增强了系统的稳定性、抗干扰能力。

基于自适应EKF的永磁同步电机无传感控制

传统扩展卡尔曼滤波(EKF)算法会受到永磁同步电机在实际运行中电机参数变化的影响,在速度的估算过程中会产生较大抖动,造成位置估算结果偏差。通过引入类Sigmoid函数来构建自适应EKF算法,用类Sigmoid函数取代传统EKF算法中关键的固定参数,实现参数的动态调整,抑制电机参数变化带来的扰动,降低超调量。建立仿真模型进行仿真验证,结果表明,自适应EKF算法相比于传统最优参数EKF算法,动态响应速度更快,抗干扰能力和鲁棒性更强,转速误差缩小了约60%,转子位置估算误差缩小了约9%。

Sensor-Based Adaptive Estimation in a Hybrid Environment Employing State Estimator Filters

It is widely acknowledged that navigation is a significant source of between sites.The Global Positioning System(GPS)has numerous navi-gational advancements,and hence it is used widely.GPS navigation can be compromised at any level between position,location,and estimation,to the detriment of the user.Consequently,a navigation system requires the precise location and underpinning tracking of an object without signal loss.The objective of a hybrid environment prediction system is to foresee the location of the user and their territory by employing a variety of sensors for position estimation and monitoring navigation.This article presents a state estimation of the relative position for indoor and outdoor activity solved with a state estimation algorithm utilizing Kalman filter.Also,a comparative study of variants of the Kalman filter,where linearizing current mean and covariance with nonlinear state estimation as an approach of Extended Kalman Filter(EFK)is applied to the collected data.The third comparative aspect uses probability distribution for the selected points with a Sigma Point Kalman Filter(SPKF)for evaluating an accelerometer,gyroscope,and GPS data in hybrid environments for various activities for different data collection scenar-ios from users.The findings of the presented model demonstrate the robust performance of all forms of the Kalman filter algorithm for diverse user-performed activities in totally contaminated indoor and outdoor environ-ments.Experimental findings with various patterns and data,conducted by different subjects using multiple modes of navigation,show that the approach can indeed lead to the intelligent development of sensor-based navigation and monitoring.State estimation and prediction is extraordinarily beneficial for mining applications,autonomous vehicle localization/tracking,and location-based services.This research work demonstrates both EKF-based and SPKF-based sensor fusion to provide an appropriate estimation.