渗透球壳流固耦合问题的位移及内力解

在弹性薄壳的线性(非线性)理论和流体力学基本方程的基础上,在小雷诺数的情况下应用相容欧拉-拉格朗日法建立了渗透球壳在流体中流固耦合的基本方程及关系式,并通过摄动法求解了渗透球壳在粘性流体中的流函数从而进一步解出其位移及内力解.通过具体算例,给出了壳的位移变形及内力图,并对有关参数进行讨论,绘出相关曲线.得出渗透系数k和流速越大的情况下,壳体位移也就越大,相应的内力也变大;球壳在三个角度处位移为零.文章能为过滤器问题提供指导.     

基于均值漂移粒子滤波的目标跟踪算法研究

针对粒子滤波计算量大的问题,将视觉跟踪领域的均值漂移算法（Mean Shift）与粒子滤波（PF）算法相结合,该算法利用均值漂移算法在重采样之后将粒子收敛到靠近目标真实状态的区域内,改善了传统粒子滤波器的退化现象,减少了算法的运行时间,通过被动跟踪仿真实例,同时使用均值漂移粒子滤波与传统粒子滤波进行跟踪仿真,分析了轨迹跟踪性能,利用均方根误差比较了误差性能。仿真结果表明,Mean Shift PF具有更高的跟踪精度,并且运行时间显著减少。     

模糊自适应滤波在车载组合导航系统中的应用

文章针对车载GPS/DR组合导航系统工作环境复杂,其观测噪声相应复杂多变的特点,引入了一种基于模糊自适应控制的卡尔曼滤波算法。这一方法基于方差匹配的思想,通过模糊控制器在线调整观测噪声方差,以改变滤波器的自适应性,从而提高导航精度。文章最后对GPS/DR系统进行了仿真,结果表明采用此方法后的滤波精度优于常规Kal-man滤波的结果。     

简化UKF算法在SINS／GPS组合导航中的应用

针对SINS／GPS组合导航系统的特点,建立了系统的非线性误差模型。根据系统状态方程为非线性而观测方程为线性的特点,将一种简化的UKF方法（Rao-Blackwellisation Additive Unscented Kalman Filter,RBAUKF）用于SINS／GPS组合导航系统中,RBAUKF采用较少的采样点数目和简化的更新算法,降低了计算复杂度。最后,在机动条件下,进行了SINS／GPS组合导航实验仿真。仿真结果表明,RBAUKF相比EKF具有更高的滤波精度,更适合在SINS／GPS组合导航系统中应用。     

基于Unscented卡尔曼滤波器的地磁辅助组合导航

将船位推算与地磁测量相结合构成组合导航方法.首先建立了推算模型,然后利用Unscented卡尔曼滤波方法,直接将地磁测量的结果用于校正推算结果,进行导航定位.此方法不采用匹配方法进行定位,可进行实时定位,能适用于非线性和离散的地磁模型.仿真表明,此方法能减小定位误差,适用于基于船位推算和地磁定位的实时组合导航.     

卡尔曼滤波在潜艇垂直面运动控制器设计中的应用

潜艇低噪声操纵存在无效用舵现象。简要介绍了滤波信号的表示形式,在潜艇垂直面线性运动方程的基础上,得出了滤波信号的系统模型,并对其进行了离散化处理;将均方差最小线性递推滤波算法引入到输入信号的控制过程中,通过仿真证明对输入信号进行卡尔曼滤波能够提高输入信号的品质,用于工程应用。     

一种船舶电力滤波器的仿真研究

在小型船舶电力推进系统中,变频驱动产生了严重的谐波污染,导致电力系统的容量大大增加,恶化电网电能质量,甚至影响了船舶安全运行.针对电力推进船舶电网存在的谐波污染问题,设计了电力滤波器的拓扑结构,制定其控制策略,分析该滤波器的工作原理,并进行计算机仿真,验证了该滤波器的有效性.     

Quasi-dynamic estimation of o–d flows from traffic counts: Formulation,statistical validation and performance analysis on real data

The paper proposes a “quasi-dynamic” framework for estimation of origin–destination (o–d) flow from traffic counts, under the assumption that o–d shares are constant across a reference period, whilst total flows leaving each origin vary for each sub-period within the reference period. The advantage of this approach over conventional within-day dynamic estimators is that of reducing drastically the number of unknowns given the same set of observed time-varying traffic counts. Obviously, the gain in accuracy depends on how realistic is the underlying assumption that total demand levels vary more rapidly over time than o–d shares. Firstly, the paper proposes a theoretical specification of the quasi-dynamic estimator. Subsequently, it proposes empirical and statistical tests to check the quasi-dynamic assumption and then compares the performances of the quasi-dynamic estimator of o–d flows with both classical off-line simultaneous dynamic estimators and on-line recursive Kalman filter-based estimators. Experiments are carried out on the real test site of A4–A23 motorways in North-Eastern Italy. Results confirm the acceptability of the assumption of quasi-dynamic o–d flows, even under the hypothesis of constant distribution shares for the whole day and show that the quasi-dynamic estimator outperforms significantly the simultaneous estimator. Data also suggest that using the quasi-dynamic estimates instead of the simultaneous estimates as historical o–d flows improves significantly the performances of the Kalman filter, which strongly depends of the quality of the seed o–d flows. In addition, it is shown that the aggregation of quasi-dynamic o–d estimates across subsequent time slices represents also the most effective way to obtain o–d estimates for larger time horizons (e.g. hourly estimates). Finally, a validation based on an hold-out sample of link flows (i.e. counts not used as inputs in the o–d estimation/updating process) revealed the quasi-dynamic estimator to be overall more robust and effective with respect to the other tested estimators.     

Dual extended Kalman filter for vehicle state and parameter estimation

The article demonstrates the implementation of a model-based vehicle estimator, which can be used for combined estimation of vehicle states and parameters. The estimator is realised using the dual extended Kalman filter (DEKF) technique, which makes use of two Kalman filters running in parallel, thus 'splitting' the state and parameter estimation problems. Note that the two problems cannot be entirely separated due to their inherent interdependencies. This technique provides several advantages, such as the possibility to switch off the parameter estimator, once a sufficiently good set of estimates has been obtained. The estimator is based on a four-wheel vehicle model with four degrees of freedom, which accommodates the dominant modes only, and is designed to make use of several interchangeable tyre models. The paper demonstrates the appropriateness of the DEKF. Results to date indicate that this is an effective approach, which is considered to be of potential benefit to the automotive industry.     

Real-time traffic state estimation in urban corridors from heterogeneous data

In recent years, rapid advances in information technology have led to various data collection systems which are enriching the sources of empirical data for use in transport systems. Currently, traffic data are collected through various sensors including loop detectors, probe vehicles, cell-phones, Bluetooth, video cameras, remote sensing and public transport smart cards. It has been argued that combining the complementary information from multiple sources will generally result in better accuracy, increased robustness and reduced ambiguity. Despite the fact that there have been substantial advances in data assimilation techniques to reconstruct and predict the traffic state from multiple data sources, such methods are generally data-driven and do not fully utilize the power of traffic models. Furthermore, the existing methods are still limited to freeway networks and are not yet applicable in the urban context due to the enhanced complexity of the flow behavior. The main traffic phenomena on urban links are generally caused by the boundary conditions at intersections, un-signalized or signalized, at which the switching of the traffic lights and the turning maneuvers of the road users lead to shock-wave phenomena that propagate upstream of the intersections. This paper develops a new model-based methodology to build up a real-time traffic prediction model for arterial corridors using data from multiple sources, particularly from loop detectors and partial observations from Bluetooth and GPS devices.