Application of Kalman Filter Method in the Forecast of Temperature in Nanchang

A temperature forecasting model was created firstly based on the Kalman filter method,and then used to predict the highest and lowest temperature in Nanchang station from October 27 to November 1,2017.Finally,according to the empirical forecasting method,guidance forecasts were established for the northern,central,and southern parts of Nanchang City.After inspection,it was found that the temperature prediction model established based on the Kalman filter method in Nanchang station had good prediction performance,and especially in the 24-hour forecast,it had advantages over the European Center.The accuracy of low temperature forecast was better than that of high temperature forecast.

基于卡尔曼(Kalman)滤波算法的体育训练视频中的运动目标检测

体育训练视频中的运动目标检测往往存在目标快速移动、目标遮挡和场景变化等问题,导致运动目标检测的难度增大。为了解决这些问题,提出了基于卡尔曼(Kalman)滤波算法的体育训练视频中运动目标检测的方法。为了提升体育训练视频图像的质量,首先对体育训练视频的图像进行一系列预处理(包括灰度变换、轮廓对比增强和噪声抑制等);然后借助混合高斯模型(Gaussian mixture module,GMM)有效提取体育训练视频的前景信息;为了精准捕捉体育训练视频中的运动目标,运用三帧差分法设定Kalman滤波器的初始状态,利用高效检测算法准确获取每一帧图像中运动目标的观测位置;随后将初始状态和观测位置的数据输入Kalman滤波器;最后在Kalman滤波器中,结合上一帧的预估值和当前帧的监测值,对体育训练视频中运动目标的当前状态进行精确估算和优化,并对下一帧的状态进行预测,从而实现了对运动目标的持续跟踪与精准预测。实验结果表明,与基于特征融合的全卷积孪生网络(siamese full convolution,Siamfc)目标追踪算法和基于连续自适应均值漂移(continuously adapting mean shift,Camshift)的均值漂移(mean shift,Meanshift)改进算法相比,采用基于Kalman滤波算法的运动目标检测方法对运动目标的重叠精度(overlap precision,OP)和中心位置误差(center location error,CLE)进行检测,不仅能够有效检测到体育训练视频中的运动目标,还可表现出较高的准确性和实时性。

Attitude sensor fault diagnosis based on Kalman filter of discrete-time descriptor system

To diagnose the fault of attitude sensors in satellites, this paper proposes a novel approach based on the Kalman filter of the discrete-time descriptor system. By regarding the sensor fault term as the auxiliary state vector, the attitude measurement system subjected to the attitude sensor fault is modeled by the discrete-time descriptor system. The condition of estimability of such systems is given. And then a Kalman filter of the discrete-time descriptor system is established based on the methodology of the maximum likelihood estimation. With the descriptor Kalman filter, the state vector of the original system and sensor fault can be estimated simultaneously. The proposed method is able to esti-mate an abrupt sensor fault as well as the incipient one. Moreover, it is also effective in the multiple faults scenario. Simulations are conducted to confirm the effectiveness of the proposed method.

基于Kalman滤波的多路温度采集系统设计

针对导航计算机中加速度计与激光陀螺仪性能易受环境温度影响,需对环境温度进行准确的测量,设计了一种基于Kalman滤波的多路温度采集系统。系统采用PT1000铂电阻作为温度传感器,通过调理电路、A/D转换电路完成信号转换,并采用一种改进Kalman算法与分段插值法相结合的方法,提高了系统运算速度并减小因硬件电路干扰产生的误差,提高了系统采集的精确度。测试结果表明,该系统温度检测在-40℃~85℃范围内,系统稳定可靠且精确度高,测量误差在±0.1℃,满足系统测温的需求。

Adaptability of n-γdiscrimination and filtering methods based on plastic scintillation

Neutrons have been extensively used in many fields,such as nuclear physics,biology,geology,medical science,and national defense,owing to their unique penetration characteristics.Gamma rays are usually accompanied by the detection of neutrons.The capability to discriminate neutrons from gamma rays is important for evaluating plastic scintillator neutron detectors because similar pulse shapes are generated from both forms of radiation in the detection system.The pulse signals measured by plastic scintillators contain noise,which decreases the accuracy of n-y discrimination.To improve the performance of n-y discrimination,the noise of the pulse signals should be filtered before the n-y discrimination process.In this study,the influences of the Fourier transform,wavelet transform,moving-average filter,and Kalman algorithm on the charge comparison method,fractal spectrum method,and back-propagation neural network methods were studied.It was found that the Fourier transform filtering algorithm exhibits better adaptability to the charge comparison method than others,with an increasing accuracy of 6.87%compared to that without the filtering process.Meanwhile,the Kalman filter offers an improvement of 3.04%over the fractal spectrum method,and the adaptability of the moving-average filter in backpropagation neural network discrimination is better than that in other methods,with an increase in 8.48%.The Kalman filtering algorithm has a significant impact on the peak value of the pulse,reaching 4.49%,and it has an insignificant impact on the energy resolution of the spectrum measurement after discrimination.

Estimation of Quaternion Motion for GPS-Based Attitude Determination Using the Extended Kalman Filter

In this paper,the Global Positioning System(GPS)interferometer provides the preliminarily computed quaternions,which are then employed as the measurement of the extended Kalman filter(EKF)for the attitude determination system.The estimated quaternion elements from the EKF output with noticeably improved precision can be converted to the Euler angles for navigation applications.The aim of the study is twofold.Firstly,the GPS-based computed quaternion vector is utilized to avoid the singularity problem.Secondly,the quaternion estimator based on the EKF is adopted to improve the estimation accuracy.Determination of the unknown baseline vector between the antennas sits at the heart of GPS-based attitude determination.Although utilization of the carrier phase observables enables the relative positioning to achieve centimeter level accuracy,however,the quaternion elements derived from the GPS interferometer are inherently noisy.This is due to the fact that the baseline vectors estimated by the least-squares method are based on the raw double-differenced measurements.Construction of the transformation matrix is accessible according to the estimate of baseline vectors and thereafter the computed quaternion elements can be derived.Using the Euler angle method,the process becomes meaningless when the angles are at 90where the singularity problem occurs.A good alternative is the quaternion approach,which possesses advantages over the equivalent Euler angle based transformation since they apply to all attitudes.Simulation results on the attitude estimation performance based on the proposed method will be presented and compared to the conventional method.The results presented in this paper elucidate the superiority of proposed algorithm.

Complementary Kalman Filter as a Baseline Vector Estimator for GPS-Based Attitude Determination

The Global Positioning System(GPS)offers the interferometer for attitude determination by processing the carrier phase observables.By using carrier phase observables,the relative positioning is obtained in centimeter level.GPS interferometry has been firstly used in precise static relative positioning,and thereafter in kinematic positioning.The carrier phase differential GPS based on interferometer principles can solve for the antenna baseline vector,defined as the vector between the antenna designated master and one of the slave antennas,connected to a rigid body.Determining the unknown baseline vectors between the antennas sits at the heart of GPS-based attitude determination.The conventional solution of the baseline vectors based on least-squares approach is inherently noisy,which results in the noisy attitude solutions.In this article,the complementary Kalman filter(CKF)is employed for solving the baseline vector in the attitude determination mechanism to improve the performance,where the receiver-satellite double differenced observable was utilized as the measurement.By using the carrier phase observables,the relative positioning is obtained in centimeter level.Employing the CKF provides several advantages,such as accuracy improvement,reliability enhancement,and real-time assurance.Simulation results based on the conventional method where the least-squares approach is involved,and the proposed method where the CKF is involved are compared and discussed.

A Novel Adaptive Kalman Filter Based on Credibility Measure

It is quite often that the theoretic model used in the Kalman filtering may not be sufficiently accurate for practical applications,due to the fact that the covariances of noises are not exactly known.Our previous work reveals that in such scenario the filter calculated mean square errors(FMSE)and the true mean square errors(TMSE)become inconsistent,while FMSE and TMSE are consistent in the Kalman filter with accurate models.This can lead to low credibility of state estimation regardless of using Kalman filters or adaptive Kalman filters.Obviously,it is important to study the inconsistency issue since it is vital to understand the quantitative influence induced by the inaccurate models.Aiming at this,the concept of credibility is adopted to discuss the inconsistency problem in this paper.In order to formulate the degree of the credibility,a trust factor is constructed based on the FMSE and the TMSE.However,the trust factor can not be directly computed since the TMSE cannot be found for practical applications.Based on the definition of trust factor,the estimation of the trust factor is successfully modified to online estimation of the TMSE.More importantly,a necessary and sufficient condition is found,which turns out to be the basis for better design of Kalman filters with high performance.Accordingly,beyond trust factor estimation with Sage-Husa technique(TFE-SHT),three novel trust factor estimation methods,which are directly numerical solving method(TFE-DNS),the particle swarm optimization method(PSO)and expectation maximization-particle swarm optimization method(EM-PSO)are proposed.The analysis and simulation results both show that the proposed TFE-DNS is better than the TFE-SHT for the case of single unknown noise covariance.Meanwhile,the proposed EMPSO performs completely better than the EM and PSO on the estimation of the credibility degree and state when both noise covariances should be estimated online.

Adaptive Kalman Filtering Approach of Color Noise in Cephalometeric Image

A kind of adaptive color noise Kalman filtering approach based on the correlative method of the system output is proposed to solve the cephalometric images of stomatology. This approach builds the color noise Kalman filtering model by adopting the equivalent measurement equation in order to aviod complicated computation and expansion of the dimension of the filter. It is also unnecessary to know the variance of measurement noise beforehand so that it is closer to the actual situation. The results of several experiments are presented to demonstrate the feasibility and good performance of this approach.

基于序贯自适应误差状态Kalman滤波的MARG姿态估计算法

在由磁传感器、加速度计和陀螺仪(MARG)组合的航姿参考系统中,加速度计和磁传感器易受线性加速度和软硬磁干扰影响,从而降低系统姿态估计精度。针对上述问题,提出一种基于欧拉角误差的序贯自适应误差状态Kalman滤波算法,用于抗干扰的MARG姿态估计。通过建立基于欧拉角误差的线性系统误差模型,避免了基于四元数或欧拉角的MARG姿态估计算法中存在的非线性问题;同时,采用序贯Sage-Husa自适应算法,通过欧拉角误差量测值实时估计量测噪声参数,抑制线性加速度和软硬磁干扰对倾角和航向角估计精度的影响。为验证所提算法有效性,进行了抗干扰实验。实验结果表明,相比无序贯自适应误差状态Kalman滤波算法,所提算法的航向角估计最大误差降低了94%以上,倾角估计最大误差降低了20%以上,提高了系统姿态估计精度,同时具有强鲁棒性。

Design of Five-Axis Camera Stabilizer Based on Quaternion Untracked Kalman Filtering Algorithm

A five-axis camera stabilizer based on quaternion unscented Kalman filter algorithm is designed. It combined the unscented Kalman filter algorithm with the quaternion attitude solution and was solved by attitude sensor. By attitude algorithm, the motor in three directions of pitch, heading and roll in the stabilizer was accurately adjusted to control the movement of the three electronic arms. In order to improve the three-axis hand-held camera stabilizer’s performance, and to solve the jitter problem of up-and-down movement not being eliminated, two mechanical anti-shake arms were loaded under the stabilizer to balance the camera’s picture in pitch, roll, heading, and above and below five directions. Movement can maintain a stable effect. The simulation results show that the algorithm can effectively suppress the attitude angle divergence and improve the attitude calculation accuracy.

Combining unscented Kalman filter and wavelet neural network for anti-slug

The stability of the subsea oil and gas production system is heavily influenced by slug flow. One successful method of managing slug flow is to use top valve control based on subsea pipeline pressure. However, the complexity of production makes it difficult to measure the pressure of subsea pipelines, and measured values are not always accessible in real-time. The research introduces a technique for integrating Unscented Kalman Filter (UKF) and Wavelet Neural Network (WNN) to estimate the state of subsea pipeline pressure using historical data and a state model. The proposed method treats multiphase flow transport as a nonlinear model, with a dynamic WNN serving as the state observer. To achieve real-time state estimation, the WNN is included into the UKF algorithm to create a WNN-based UKF state equation. Integrate WNN and UKF in a novel way to predict system state accurately. The simulated results show that the approach can efficiently predict the inlet pressure and manage the slug flow in real-time using the riser's top pressure, outlet flow and valve opening. This method of estimate can significantly increase the control effect.

一种基于虚拟星敏感器的卫星姿态确定方法

对于配置多个星敏感器的卫星,采用常规的定常增益卡尔曼滤波方法进行姿态确定时,存在滤波定常增益矩阵众多、系统复杂的问题,为了简化滤波系统设计,统一定常增益矩阵,提出一种基于虚拟星敏感器的姿态确定方法.给出一种计算量小、适合星载计算机在轨实时计算的星敏感器时间滞后补偿及相对基准标定算法,将星敏感器的输出数据统一到当前星时,同时将星敏感器的测量基准统一.基于单星敏/双星敏的输出数据构造虚拟星敏感器(安装矩阵为单位阵)的输出数据,设计统一的定常增益矩阵进行姿态确定.仿真结果表明,本方法与常规的定常增益卡尔曼滤波方法姿态确定精度相当,从而验证该方法有效,且具有重要的工程应用价值.

九轴MEMS微型AHRS设计及其在矢量水听器中的应用

为了精确测量矢量水听器的姿态,设计了一种体积为20 mm×20 mm×5 mm、重量为1.8 g的微型航姿参考系统(AHRS)。利用微机电系统(MEMS)陀螺仪测定角速度,用四元数等效旋转矢量算法求解姿态角;采用扩展卡尔曼滤波器对姿态角估计值进行修正,并估计出MEMS陀螺仪的角速度漂移量。测试结果表明,所提AHRS的俯仰、横滚和航向角误差均方根值分别为0.04°、0.04°和0.34°。最后,将AHRS集成到矢量水听器中,并在加窗直方图统计波达方向估计算法中加入姿态修正。海上实验结果表明,对于航速为8 kn的目标船,经AHRS修正后的单矢量水听器方位角误差均方根值约为3.8°,减小了平台运动导致的方位测量误差。

事件触发机制下配电网三相动态状态估计

随着高级量测体系的发展和智能电表的广泛应用,为配电网三相状态估计提供了丰富的终端量测信息。与此同时,大量的智能电表数据给配电网通信系统提出了更高的通信带宽和实时存储要求。为了缓解量测拥堵和时延现象,文中引入事件触发机制代替传统量测数据的周期性采样,在保证有效量测信息及时上传的同时减少通信成本和投资。在此基础上,针对配电网实时状态感知问题,提出了基于鲁棒集合卡尔曼滤波的配电网三相动态状态估计方法,在正常运行场景下,能够保持与无偏估计加权最小二乘法相近的估计精度;当含有坏数据时,该方法也拥有较强的鲁棒性。

基于MACF-CKF多传感器融合的姿态解算算法

针对惯性导航测量单元姿态解算精度低的问题,提出了一种基于多传感器隶属度自适应互补滤波(Membership Adaptive Complementary Filtering,MACF)和容积卡尔曼滤波(Cubature Kalman Filter,CKF)相融合的姿态解算算法。使用指数加权移动平均修正陀螺仪噪声偏差,为了避免出现陀螺仪和加速度计的权重在互补滤波中分配不当而导致俯仰角和横滚角误差较大的现象,通过构造陀螺仪偏差的隶属度函数,判断互补滤波对陀螺仪的信任度,根据信任度动态调整互补滤波自适应因子,同时用磁力计和陀螺仪进行CKF来解决航向角发散的问题。实验表明:所提出的算法无论在静态条件还是动态条件下均能快速、准确实现姿态解算,在动态车载实验中,横滚角和俯仰角精度分别提升了24.5%和63.2%,航向角提升了48.8%,可以保证解算精度。

融合自适应步长与地图匹配的智能手机PDR室内定位算法

针对经典的PDR算法中步长估算模型无法适应不同个体之间差异以及手机内部传感器航向角估算不准确的问题,提出了一种融合自适应步长与地图匹配的智能手机PDR室内定位算法。该算法在Weinberg模型的基础上,引入相邻波峰时间差函数以提高步长模型的适应性。采用双阈值法进行步频检测,并利用卡尔曼滤波优化地图匹配后的航向角,最终实现室内行人的精确定位。实验数据显示,所提算法总路程的闭环误差为0.58%,与仅使用自适应步长相比下降了73.29%,与仅使用地图匹配相比下降了59.21%,行程72 m的累积误差为4.484 m,相较于经典的PDR算法下降了28.21%。改进后的算法定位精度显著提高,且闭环误差和累积误差更小。实验结果表明,该算法具有良好的工程应用价值。

误差状态卡尔曼滤波的视觉惯性自适应融合定位方法研究

头盔瞄准具(HMS)是近年来新一代战斗机飞行员的辅助瞄准设备,能够帮助飞行员增强战场态势感知能力,对敌方目标进行快速、精准打击。其能正常工作的关键是获取飞行员头部相对于运动飞机的姿态参数。本文结合头盔瞄准具这一应用场景研究了视觉组合姿态测量关键技术。视觉惯性组合定位能够实现目标位姿测量方法的优势互补,而由于标称噪声矩阵无法绝对准确预测,融合算法的鲁棒性、精度有待进一步提升。针对这一问题,本文提出一种误差状态卡尔曼滤波框架下基于变分贝叶斯推断的视觉惯性自适应融合方法。首先,对于过程噪声使用逆威沙特(Wishart)分布进行建模,之后通过引入隐变量分解一步预测协方差,并结合变分贝叶斯推断实现了对过程噪声协方差矩阵的在线估计。试验证明,在复杂运动及标称噪声协方差矩阵偏移较大的测量条件下,所提位姿测量算法具有较高的精度与鲁棒性,能够完成对靶标的快速、高精度跟踪。

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。

基于卡尔曼滤波算法的电池状态估计

为更好地获得锂离子电池荷电状态SOC(state-of-charge)估计值,选用二阶等效电路模型作为研究对象,针对带有遗忘因子的递推最小二乘法在参数辨识中易受到噪声等环境因素干扰的缺点,提出偏差补偿最小二乘法来实现模型参数的准确辨识,并结合无迹卡尔曼滤波算法对SOC进行估计。针对无迹卡尔曼滤波算法稳定性差等缺点,提出利用权重向量更新滤波算法中的卡尔曼滤波增益。实验结果表明,所提算法估计SOC的总误差可控制在2.7%以内,验证了算法的鲁棒性和有效性。