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。     

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.     

基于卡尔曼滤波的4WD电动车辆寄生功率的测量技术

根据串励直流电动机工作特性,利用车辆行驶过程中较易测得的驱动电机工作电流及驱动轮转速,运用MATLAB/SIMULINK软件,建立基于卡尔曼滤波的驱动轮滑转率模型和寄生功率模型并进行仿真;通过道路试验对电动机工作电流和驱动轮转速的检测,实时预测驱动轮的滑转率与产生的寄生功率。结果表明,驱动轮滑转率和寄生功率的仿真结果与试验结果非常接近。     

存在车辆干扰的车道线识别

为避免道路上行驶的其它车辆对车道线识别的干扰,提出了一种结合车辆识别的车道线识别方法。融合雷达数据,车辆识别模块首先在图像中识别出车辆占据的区域;对于每一个车道线识别模块挑出的车道线候选点进行判断,去除处于车辆区域的车道线点;如果有效车道线点数目不足,则利用卡尔曼滤波的跟踪结果,确定符合最小风险函数的车道线位置。经过多种工况下的试验验证,该方法能够稳定地对车道线进行识别,准确地提取车道线参数,并且算法对车辆干扰有良好的抵抗能力。     

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.     

Integrated solution for anomalous driving detection based on BeiDou/GPS/IMU measurements

There has been an increasing role played by Global Navigation Satellite Systems (GNSS) in Intelligent Transportation System (ITS) applications in recent decades. In particular, centimeter/decimetre positioning accuracy is required for some safety related applications, such as lane control, collision avoidance, and intelligent speed assistance. Lane-level Anomalous driving detection underpins these safety-related ITS applications. The two major issues associated with such detection are (1) accessing high accuracy vehicle positioning and dynamic parameters; and (2) extraction of irregular driving patterns from such information. This paper introduces a new integrated framework for detecting lane-level anomalous driving, by combining Global Positioning Systems (GPS), BeiDou, and Inertial Measurement Unit (IMU) with advanced algorithms. Specifically, we use Unscented Particle Filter (UPF) to perform data fusion with different positioning sources. The detection of different types of Anomalous driving is achieved based on the application of a Fuzzy Inference System (FIS) with a newly introduced velocity-based indicator. The framework proposed in this paper yield significantly improved accuracy in terms of positioning and Anomalous driving detection compared to state-of-the-art, while offering an economically viable solution for performing these tasks.     

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.     

强跟踪滤波在动基座传递对准中的应用

在保证捷联惯性导航系统（SINS）动基座传递对准具有一定精度的前提下,为了缩短传递对准的时间,对强跟踪滤波（STF）在动基座传递对准中的应用进行了研究。通过在一步预测方差P（k＋1｜k）中引入渐消因子λ（k＋1）,以此减弱老数据对当前滤波值的影响。同时,对渐消因子λ（k＋1）的计算做出修正,使得状态估计更加平滑。最后,在匀速拐弯的机动方式下进行了仿真,结果表明强跟踪滤波在对准精度与速度上比Kalman滤波有更好的表现。     

利用ICCP的水下地磁修正惯导方法研究

迭代最近等值线算法（ICCP）是一种重要的地磁匹配导航算法。首先,为了获取相对精度较高的位置信息,又在一定程度上达到弥补实测数据精度不足的目的,将二次插值技术应用到地磁匹配算法中;然后,仿真计算得到了最佳匹配位置;最后,针对目前水下航行器通过航位推算等传统方法实现惯导重调的弊端,提出将地磁匹配位置误差作为观测量,用卡尔曼滤波对惯导系统的速度、位置和姿态误差进行最优估计。由最后仿真结果表明,以磁场匹配位置误差作为观测量来估计惯导误差是可行的。     

一种单脉冲末制导雷达抗质心干扰算法

为了提高单脉冲末制导雷达的抗质心干扰能力,结合信号到达方向（DOA）的极大似然估计法和卡尔曼滤波,提出了一种新的算法。首先,在假设目标和干扰数量、参数已知的情况下,建立雷达多次接收信号匹配滤波后跟踪回波附近的多个相邻采样点的似然函数。其次,计算得到目标和干扰信号DOA、延迟和功率的极大似然估计,并应用最小描述长度（MDL）准则判断目标和干扰数量。最后,设计了合适的卡尔曼滤波器用于正确跟踪目标。仿真结果验证了算法的有效性。