Software alignment of the BESⅢ main drift chamber using the Kalman Filter method

Software alignment is quite important for a tracking detector to reach its ultimate position accuracy and momentum resolution. We developed a new alignment algorithm for the BESⅢ Main Drift Chamber using the Kalman Filter method. Two different types of data which are helix tracks and straight tracks are used to test this algorithm, and the results show that the design and implementation is successful.

Hydraulic model with roughness coefficient updating method based on Kalman filter for channel flood forecast

A real-time channel flood forecast model was developed to simulate channel flow in plain rivers based on the dynamic wave theory. Taking into consideration channel shape differences along the channel, a roughness updating technique was developed using the Kalman filter method to update Manning＇s roughness coefficient at each time step of the calculation processes. Channel shapes were simplified as rectangles, triangles, and parabolas, and the relationships between hydraulic radius and water depth were developed for plain rivers. Based on the relationship between the Froude number and the inertia terms of the momentum equation in the Saint-Venant equations, the relationship between Manning＇s roughness coefficient and water depth was obtained. Using the channel of the Huaihe River from Wangjiaba to Lutaizi stations as a case, to test the performance and rationality of the present flood routing model, the original hydraulic model was compared with the developed model. Results show that the stage hydrographs calculated by the developed flood routing model with the updated Manning＇s roughness coefficient have a good agreement with the observed stage hydrographs. This model performs better than the original hydraulic model.

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。

Generalized cubature quadrature Kalman filters:derivations and extensions

A new Gaussian approximation nonlinear filter called generalized cubature quadrature Kalman filter (GCQKF) is introduced for nonlinear dynamic systems. Based on standard GCQKF, two extensions are developed, namely square root generalized cubature quadrature Kalman filter (SR-GCQKF) and iterated generalized cubature quadrature Kalman filter (I-GCQKF). In SR-GCQKF, the QR decomposition is exploited to alter the Cholesky decomposition and both predicted and filtered error covariances have been propagated in square root format to make sure the numerical stability. In I-GCQKF, the measurement update step is executed iteratively to make full use of the latest measurement and a new terminal criterion is adopted to guarantee the increase of likelihood. Detailed numerical experiments demonstrate the superior performance on both tracking stability and estimation accuracy of I-GCQKF and SR-GCQKF compared with GCQKF.

Coupling Ensemble Kalman Filter with Four-dimensional Variational Data Assimilation

This study examines the performance of coupling the deterministic four-dimensional variational assimilation system （4DVAR） with an ensemble Kalman filter （EnKF） to produce a superior hybrid approach for data assimilation. The coupled assimilation scheme （E4DVAR） benefits from using the state-dependent uncertainty provided by EnKF while taking advantage of 4DVAR in preventing filter divergence： the 4DVAR analysis produces posterior maximum likelihood solutions through minimization of a cost function about which the ensemble perturbations are transformed, and the resulting ensemble analysis can be propagated forward both for the next assimilation cycle and as a basis for ensemble forecasting. The feasibility and effectiveness of this coupled approach are demonstrated in an idealized model with simulated observations. It is found that the E4DVAR is capable of outperforming both 4DVAR and the EnKF under both perfect- and imperfect-model scenarios. The performance of the coupled scheme is also less sensitive to either the ensemble size or the assimilation window length than those for standard EnKF or 4DVAR implementations.

Study of nonlinear filter methods: particle filter

Extended Kalman filter （EKF） is one of the most widely used methods for nonlinear system estimation. A new filtering algorithm, called particle filtering （PF） is introduced. PF can yield better performance than that of EKF, because PF does not involve the linearization approximating to nonlinear systems, that is required by the EKF. PF has been shown to be a superior alternative to the EKF in a variety of applications. The base idea of PF is the approximation of relevant probabifity distributions using the concepts of sequential importance sampling and approximation of probability distributions using a set of discrete random samples with associated weights. PF methods still need to be improved in the aspects of accuracy and calculating speed.

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.

Adaptive Kalman Filter of Transfer Alignment with Un-modeled Wing Flexure of Aircraft

The alignment accuracy of the strap-down inertial navigation system （SINS） of airborne weapon is greatly degraded by the dynamic wing flexure of the aircraft. An adaptive Kalman filter uses innovation sequences based on the maximum likelihood estimated criterion to adapt the system noise covariance matrix and the measurement noise covariance matrix on line, which is used to estimate the misalignment if the model of wing flexure of the aircraft is unknown. From a number of simulations, it is shown that the accuracy of the adaptive Kalman filter is better than the conventional Kalman filter, and the erroneous misalignment models of the wing flexure of aircraft will cause bad estimation results of Kalman filter using attitude match method.

Estimation and Forecasting Survival of Diabetic CABG Patients (Kalman Filter Smoothing Approach)

In this paper, we present a new approach (Kalman Filter Smoothing) to estimate and forecast survival of Diabetic and Non Diabetic Coronary Artery Bypass Graft Surgery (CABG) patients. Survival proportions of the patients are obtained from a lifetime representing parametric model (Weibull distribution with Kalman Filter approach). Moreover, an approach of complete population (CP) from its incomplete population (IP) of the patients with 12 years observations/follow-up is used for their survival analysis [1]. The survival proportions of the CP obtained from Kaplan Meier method are used as observed values yt?at time t (input) for Kalman Filter Smoothing process to update time varying parameters. In case of CP, the term representing censored observations may be dropped from likelihood function of the distribution. Maximum likelihood method, in-conjunction with Davidon-Fletcher-Powell (DFP) optimization method [2] and Cubic Interpolation method is used in estimation of the survivor’s proportions. The estimated and forecasted survival proportions of CP of the Diabetic and Non Diabetic CABG patients from the Kalman Filter Smoothing approach are presented in terms of statistics, survival curves, discussion and conclusion.

MLP training in a self-organizing state space model using unscented Kalman particle filter

Many Bayesian learning approaches to the multi-layer perceptron （MLP） parameter optimization have been proposed such as the extended Kalman filter （EKF）. This paper uses the unscented Kalman particle filter （UPF） to train the MLP in a self- organizing state space （SOSS） model. This involves forming augmented state vectors consisting of all parameters （the weights of the MLP） and outputs. The UPF is used to sequentially update the true system states and high dimensional parameters that are inherent to the SOSS moder for the MLP simultaneously. Simulation results show that the new method performs better than traditional optimization methods.

SELF-TUNING MEASUREMENT FUSION KALMAN FILTER WITH CORRELATED MEASUREMENT NOISES

For the multisensor system with correlated measurement noises and unknown noise statistics, based on the solution of the matrix equations for correlation function, the on-line estimators of the noise variances and cross-covariances is obtained. Further, a self-tuning weighted measurement fusion Kalman filter is presented, based on the Riccati equation. By the Dynamic Error System Analysis (DESA) method, it rigorously proved that the presented self-tuning weighted measurement fusion Kalman filter converges to the optimal weighted measurement fusion steady-state Kalman filter in a realization or with probability one, so that it has asymptotic global optimality. A simulation example for a target tracking system with 3-sensor shows that the presented self-tuning measurement fusion Kalman fuser converges to the optimal steady-state measurement fusion Kalman fuser.

两传感器最优信息融合Kalman滤波器及其在跟踪系统中的应用

针对两传感器信息融合 ,提出了一种在标量加权下的最优信息融合 Kalm an滤波器。该信息融合滤波器考虑了局部滤波误差的相关性 ,避免了局部滤波误差方差阵的逆矩阵的计算 ,也避免了加权矩阵的计算 ,只要求计算加权系数 ,便于实时应用。一个跟踪系统的仿真例子验证了其有效性。

Kalman滤波方法在黑河出山径流年平均流量预报中的应用

应用Ｋａｌｍａｎ滤波方法，以流域平均降水量、气温、太阳黑子相对数及莺落峡站６年显著周期序列等为控制参数。

基于Kalman-ARIMA模型的大坝变形预测

大坝变形预测是大坝安全监测系统的关键组成部分,对监控大坝的安全运行起着关键作用。然而,大坝变形监测数据易受到随机干扰噪声的污染,影响变形预测的精度。故提出了Kalman-ARIMA模型,即先根据卡尔曼滤波法剔除观测数据中的随机干扰噪声,然后利用ARIMA模型对经过滤波后的数据进行建模并作预测。结合某大坝的位移监测数据资料,利用Kalman-ARIMA模型预测了该大坝的位移,并与仅利用ARIMA模型预测的位移值作对比,结果表明,Kalman-ARIMA模型能够有效地降低预测值与真实值之间的误差,可以应用于大坝变形预测。

基于Kalman滤波的气体超声波流量计融合方法

针对气体超声波流量计中多个声道获得的多个流量结果如何融合的问题,提出了基于Kalman滤波的融合方法。把多声道气体超声波流量计看成是若干个单声道流量计的组合,根据每个声道计算结果的统计规律,自动地调整每个声道的流量结果在最终输出中的权重系数,在消耗较小的计算资源情况下,实现对流量结果中异常值的判断和处理,使流量输出更加稳定。在以DSP和FPGA为核心的气体超声流量计信号处理系统上实时实现算法,并进行实流实验。实验结果验证了方法的有效性。

基于Kalman滤波的信息融合白噪声最优反卷积滤波器

应用Kalman滤波方法 ,基于Riccati方程 ,在线性最小方差最优信息融合准则下 ,提出了两传感器最优信息融合白噪声反卷积滤波器。同单传感器情形相比 ,可提高滤波精度。它可应用于石油地震勘探信号处理。一个信息融合Bernoulli

基于卡曼滤波的矿浆浓度计设计

设计了一种基于卡曼滤波的γ射线浓度计。通过分析射线浓度计的测量原理,建立了射线强度与矿浆浓度的关系式;利用卡曼滤波对射线强度进行最优估计,以抑制测量结果的扰动;最后通过最小二乘法分段拟合计算对应的浓度。结果表明,该浓度计测量及时、准确,最大误差0.328个百分点,均方根误差0.194个百分点,测量精度得到有效提高。

Kalman滤波在气象数据同化中的发展与应用

气象学领域各种观测 (特别是遥感遥测等非常规观测 )数据的大量增多和数值天气预报模式的不断进步 ,推动气象数据同化技术不断发展。回顾了 Kalman滤波在气象数据同化中的引入和几个发展阶段 ;介绍了 Kalman滤波 (尤其是简化 Kalman滤波和总体 Kalman滤波 )在气象数据同化中的重要地位和应用进展。

基于Kalman滤波的通用和统一的白噪声估计方法

用射影理论,基于Kalman滤波提出了通用和统一的白噪声估计方法,可统一解决带非零均值相关噪声的线性离散时变随机控制系统的白噪声滤波、平滑和预报问题.提出了输入白噪声估值器和观测白噪声估值器,最优和稳态白噪声估值器,固定点、固定滞后和固定区间白噪声平滑器,白噪声新息滤波器和Wiener滤波器.它可应用于石油地震勘探信号处理和状态估计,为解决信号和状态估计问题,提供了新的途径和工具.关于Bernoulli-Gaussian白噪声估值器的仿真例子说明了其有效性.

基于Kalman滤波的GPS水汽层析方法及其应用

开发了基于Kalman滤波的GPS水汽层析方法,Kalman滤波提供了一种高效可计算的方法来估计过程的状态。将这种方法应用于海南地区GPS小网观测试验中,成功地层析出观测站上空大气水汽的垂直结构。结果表明,GPS层析得到的水汽廓线信息与探空符合较好,即使水汽先验估计存在±50%偏差的情况下,依然能获取正确、可靠的水汽垂直结构信息。通过初步分析认为,层析结果较稳定的原因可能是因为这种方法在一定程度上避免了层析方程解算过程中的病态问题,且对水汽先验信息并不敏感,使得层析结果更忠实于原始GPS观测资料。