线性系统在非线性等式约束下的集员卡尔曼滤波

为了解决运行系统要求的100%估计包含真实最小域的需要和计算机的实时计算问题,使用集员卡尔曼滤波算法解决非线性状态约束.采用最小迹椭球算法对状态向量的椭球域进行更新,同时分别在预测和滤波的2个阶段用投影的方法把没有约束的状态估计投影到有约束的状态估计表面来处理约束问题.最后将所设计的集员卡尔曼滤波器应用到汽车追踪实例中.实验结果表明:采用所提算法的系统只是在初始值有比较大的误差,在后续的跟踪过程中都能在最大最小界之内跟踪上真实值.所提算法相比于传统的卡尔曼算法,其误差能迅速减小.实例仿真结果证明了所提方法的可行性和有效性.

集员卡尔曼滤波器在电机故障诊断中的应用

本文针对伺服电机提出了一种基于集员卡尔曼滤波器的故障诊断方法并进行了实际应用.首先,基于运动学关系和数据驱动技术构建了伺服电机的综合系统动态.之后,针对电机的位置–速度模型设计了一个中心对称多面体卡尔曼滤波器来检测位置传感器和速度传感器是否发生故障.同时,针对电机的力矩–速度模型设计了一个中心对称多面体卡尔曼滤波器来检测力矩执行器故障和速度传感器故障.然后,基于上述的两个中心对称多面体卡尔曼滤波器提出了一种故障隔离律,可以定位出伺服电机故障部件的位置.最后,通过一个实际的伺服电机实验平台对所提出方法进行了应用,并验证了其可行性与有效性.

基于集员卡尔曼滤波的智能车定位研究

本文针对道路约束的实际问题,应用集员卡尔曼滤波算法对具有状态约束的智能车进行研究.以具有行驶路线约束的Freescale 16位单片机MC9S12DG128B控制的智能模型车为控制对象进行集员卡尔曼滤波算法设计和应用,实验结果表明该算法能有效地减小估计误差,也能够很好的满足实际应用的要求.

融合多道面数据挖掘算法的交通安全监测系统

为了提高系统的交通事故监测性能,首先通过卡尔曼集进行快速事故预测,其次引入参数事故覆盖范围,利用多道面状态传感器节点数据挖掘实现对事故的有效筛选,随后结合数据挖掘实现对根源交通事故的有效监测。最后,通过仿真结果表明:该系统能准确地检测出各种交通运行参数和交通事件,为交通安全提供了有力保障。

旋转导向钻井工具姿态测量陀螺仪故障估计与处理方法

针对旋转导向钻井工具姿态测量系统陀螺仪故障问题,提出一种陀螺仪加性故障估计与处理方法。首先将陀螺仪故障增广为状态变量,融合加速度计测量信息建立非线性测量模型;然后针对由于模型线性化、陀螺仪漂移、钻井过程的高温、高压、强振动等因素导致卡尔曼滤波算法估计精度变差的问题,将测量误差等效为幅值有界但分布未知的误差,提出了一种自适应扩展集员卡尔曼滤波算法,实现了陀螺仪加性故障的准确估计,利用陀螺仪故障估计值校正陀螺仪输出,实现故障条件下工具面角的准确测量。样机模拟实验结果表明在发生陀螺仪故障情况下,基于所提出的算法,工具面角估计值的最大均方根误差为3.841°,满足实际钻井技术需求,为提高旋转导向钻井工具姿态测量系统的可靠性奠定了基础。

Fuzzy adaptive Kalman filter for indoor mobile target positioning with INS/WSN integrated method

Pure inertial navigation system(INS) has divergent localization errors after a long time. In order to compensate the disadvantage, wireless sensor network(WSN) associated with the INS was applied to estimate the mobile target positioning. Taking traditional Kalman filter(KF) as the framework, the system equation of KF was established by the INS and the observation equation of position errors was built by the WSN. Meanwhile, the observation equation of velocity errors was established by the velocity difference between the INS and WSN, then the covariance matrix of Kalman filter measurement noise was adjusted with fuzzy inference system(FIS), and the fuzzy adaptive Kalman filter(FAKF) based on the INS/WSN was proposed. The simulation results show that the FAKF method has better accuracy and robustness than KF and EKF methods and shows good adaptive capacity with time-varying system noise. Finally, experimental results further prove that FAKF has the fast convergence error, in comparison with KF and EKF methods.

A Homogeneous Linear Estimation Method for System Error in Data Assimilation

In this paper, a new bias estimation method is proposed and applied in a regional ensemble Kalman filter （EnKF） based on the Weather Research and Forecasting （WRF） Model. The method is based on a homogeneous linear bias model, and the model bias is estimated using statistics at each assimilation cycle, which is different from the state augmentation methods proposed in pre- vious literatures. The new method provides a good estimation for the model bias of some specific variables, such as sea level pres- sure （SLP）. A series of numerical experiments with EnKF are performed to examine the new method under a severe weather condi- tion. Results show the positive effect of the method on the forecasting of circulation pattern and meso-scale systems, and the reduc- tion of analysis errors. The background error covarianee structures of surface variables and the effects of model system bias on EnKF are also studied under the error covariance structures and a new concept ＇correlation scale＇ is introduced. However, the new method needs further evaluation with more cases of assimilation.

Assimilating ASAR Data for Estimating Soil Moisture Profile Using an Ensemble Kalman Filter

Active microwave remote sensing data were used to calculate the near-surface soil moisture in the vegetated areas.In this study,Advanced Synthetic Aperture Radar(ASAR)observations of surface soil moisture content were used in a data assimilation framework to improve the estimation of the soil moisture profile at the middle reaches of the Heihe River Basin,Northwest China.A one-dimensional soil moisture assimilation system based on the ensemble Kalman filter(EnKF),the forward radiative transfer model,crop model,and the Distributed Hydrology-Soil-Vegetation Model(DHSVM)was developed.The crop model,as a semi-empirical model,was used to estimate the surface backscattering of vegetated areas.The DHSVM is a distributed hydrology-vegetation model that explicitly represents the effects of topography and vegetation on water fluxes through the landscape.Numerical experiments were conducted to assimilate the ASAR data into the DHSVM and in situ soil moisture at the middle reaches of the Heihe River Basin from June20 to July 15,2008.The results indicated that EnKF is effective for assimilating ASAR observations into the hydrological model.Compared with the simulation and in situ observations,the assimilated results were significantly improved in the surface layer and root layer,and the soil moisture varied slightly in the deep layer.Additionally,EnKF is an efficient approach to handle the strongly nonlinear problem which is practical and effective for soil moisture estimation by assimilation of remote sensing data.Moreover,to improve the assimilation results,further studies on obtaining more reliable forcing data and model parameters and increasing the efficiency and accuracy of the remote sensing observations are needed,also improving estimation accuracy of model operator is important.

Probabilistic data association algorithm based on ensemble Kalman filter with observation iterated update

Aiming at improving the observation uncertainty caused by limited accuracy of sensors,and the uncertainty of observation source in clutters,through the dynamic combination of ensemble Kalman filter(EnKF) and probabilistic data association(PDA),a novel probabilistic data association algorithm based on ensemble Kalman filter with observation iterated update is proposed.Firstly,combining with the advantages of data assimilation handling observation uncertainty in EnKF,an observation iterated update strategy is used to realize optimization of EnKF in structure.And the object is to further improve state estimation precision of nonlinear system.Secondly,the above algorithm is introduced to the framework of PDA,and the object is to increase reliability and stability of candidate echo acknowledgement.In addition,in order to decrease computation complexity in the combination of improved EnKF and PDA,the maximum observation iterated update mechanism is applied to the iteration of PDA.Finally,simulation results verify the feasibility and effectiveness of the proposed algorithm by a typical target tracking scene in clutters.

The Performance Evaluation of the Integration of Inertial Navigation System and Global Navigation Satellite System with Analytic Constraints

The integration of GNSS （Global Navigation Satellite System） and INS （Inertial Navigation System） using IMU （Inertial Measurement Unit） is now widely used for MMS （Mobile Mapping System） and navigation applications to seamlessly determine position, velocity and attitude of the mobile platform. With low cost, small size, ligh weight and low power consumtion, the MEMS （Micro-Electro-Mechanical System） IMU and low cost GPS （Global Positioning System） receivers are now the trend in research and using for many applications. However, researchs in the literature indicated that the the performance of the low cost INS/GPS systems is still poor, particularly, in case of GNSS-noise environment. To overcome this problem, this research applies analytic contrains including non-holonomic constraint and zero velocity update in the data fusion engine such as Extended Kalman Filter to improve the performance of the system. The benefit of the proposed method will be demonstrated through experiments and data analysis.

COOPERATIVE SHIFT ESTIMATION OF TARGET TRAJECTORY USING CLUSTERED SENSORS

In this paper,a mathematical model for target tracking using nonlinear scalar range sensors is formulated first.A time-shift sensor scheduling strategy is addressed on the basis of a k-barrier coverage protocol and all the sensors are divided into two classes of clusters,active cluster,and submissive cluster,for energy-saving.Then two types of time-shift nonlinear filters are proposed for both active and submissive clusters to estimate the trajectory of the moving target with disturbed dynamics.The stochastic stability of the two filters is analyzed.Finally,some numerical simulations are given to demonstrate the effectiveness of the new filters with a comparison of EKF.

Comparison between gradient based UCODE2005 and the ensemble Kalman Filter for transient groundwater flow inverse modeling

Gradient based UCODE_2005 and data assimilation based on the Ensemble Kalman Filter(EnKF) are two different inverse methods. A synthetic two-dimensional flow case with four no-flow boundaries is used to compare the UCODE_2005 with the Ensemble Kalman Filter(EnKF) for their efficiency to inversely calculate and calibrate a hydraulic conductivity field based on hydraulic head data. A zonal, random heterogeneous conductivity field is calibrated by assimilating the time series of heads observed in monitoring wells. The study results indicate that the two inverse methods, UCODE_2005 and EnKF, could be used to calibrate the hydraulic conductivity field to a certain degree. More available observations and information about the conductivity field, more accurate inverse results will be obtained for the UCODE_2005. On the other hand, for a realistic zonal heterogeneous hydraulic conductivity field, EnKF can only efficiently determine the hydraulic conductivity field at the first several assimilated time steps. The results obtained by the UCODE_2005 look better than those by the EnKF. This is possibly due to the fact that the UCODE_2005 uses observed head data at every time step, while EnKF can only use observed heads at first several steps due to the filter divergence problem.

Simultaneous estimation of surface soil moisture and soil properties with a dual ensemble Kalman smoother

In this paper, a new state-parameter estimation approach is presented based on the dual ensemble Kalman smoother(DEn KS) and simple biosphere model(Si B2) to sequentially estimate both the soil properties and soil moisture profile by assimilating surface soil moisture observations. The Arou observation station, located in the upper reaches of the Heihe River in northwestern China, was selected to test the proposed method. Three numeric experiments were designed and performed to analyze the influence of uncertainties in model parameters, atmospheric forcing, and the model's physical mechanics on soil moisture estimates. Several assimilation schemes based on the ensemble Kalman filter(En KF), ensemble Kalman smoother(En KS), and dual En KF(DEn KF) were also compared in this study. The results demonstrate that soil moisture and soil properties can be simultaneously estimated by state-parameter estimation methods, which can provide more accurate estimation of soil moisture than traditional filter methods such as En KF and En KS. The estimation accuracy of the model parameters decreased with increasing error sources. DEn KS outperformed DEn KF in estimating soil moisture in most cases, especially where few observations were available. This study demonstrates that the DEn KS approach is a useful and practical way to improve soil moisture estimation.