An innovative localisation algorithm for railway vehicles

In modern railway automatic train protection and automatic train control systems, odometry is a safety relevant on-board subsystem which estimates the instantaneous speed and the travelled distance of the train; a high reliability of the odometry estimate is fundamental, since an error on the train position may lead to a potentially dangerous overestimation of the distance available for braking. To improve the odometry estimate accuracy, data fusion of different inputs coming from a redundant sensor layout may be used. The aim of this work has been developing an innovative localisation algorithm for railway vehicles able to enhance the performances, in terms of speed and position estimation accuracy, of the classical odometry algorithms, such as the Italian Sistema Controllo Marcia Treno (SCMT). The proposed strategy consists of a sensor fusion between the information coming from a tachometer and an Inertial Measurements Unit (IMU). The sensor outputs have been simulated through a 3D multibody model of a railway vehicle. The work has provided the development of a custom IMU, designed by ECM S.p.a, in order to meet their industrial and business requirements. The industrial requirements have to be compliant with the European Train Control System (ETCS) standards: the European Rail Traffic Management System (ERTMS), a project developed by the European Union to improve the interoperability among different countries, in particular as regards the train control and command systems, fixes some standard values for the odometric (ODO) performance, in terms of speed and travelled distance estimation. The reliability of the ODO estimation has to be taken into account basing on the allowed speed profiles. The results of the currently used ODO algorithms can be improved, especially in case of degraded adhesion conditions; it has been verified in the simulation environment that the results of the proposed localisation algorithm are always compliant with the ERTMS requirements. The estimation strategy has good performance also under degraded adhesion conditions and could be put on board of high-speed railway vehicles; it represents an accurate and reliable solution. The IMU board is tested via a dedicated Hardware in the Loop (HIL) test rig: it includes an industrial robot able to replicate the motion of the railway vehicle. Through the generated experimental outputs the performances of the innovative localisation algorithm have been evaluated: the HIL test rig permitted to test the proposed algorithm, avoiding expensive (in terms of time and cost) on-track tests, obtaining encouraging results. In fact, the preliminary results show a significant improvement of the position and speed estimation performances compared to those obtained with SCMT algorithms, currently in use on the Italian railway network.     

基于深度可分离卷积和多级特征金字塔网络的行人检测

为提高行人检测的准确率,提出一种基于卷积神经网络的行人检测方法。该方法以YOLOv3-tiny算法为基础,在骨干网络部分,用深度可分离卷积的网络结构代替原卷积网络结构,加深网络深度。在检测部分,提出一种改进的多级特征金字塔网络,该网络由8个结构相同的使用深度可分离卷积的特征金字塔组成,特征金字塔之间串联连接,将不同金字塔得到的相同尺寸的特征进行融合,利用融合后的特征金字塔进行检测。在Caltech Pedestrian数据集上进行测试。结果表明:该方法的漏检率为57.83%,比梯度方向直方图(HOG)方法低32.53%,比基于深度学习的方法SA Fast-RCNN和MS-CNN分别低4.67%、3.21%;运行速度为34 ms/帧。因而,该方法满足了实时性要求。     

Pedestrian crossing detection based on evidential fusion of video-sensors

This paper introduces an online pedestrian crossing detection system that uses pre-existing traffic-oriented video-sensors which, at regular intervals, provide coarse spatial measurements on areas along a crosswalk. Pedestrian crossing detection is based on the recognition of occupancy patterns induced by pedestrians when they move on the crosswalk. In order to improve the ability of non-dedicated sensors to detect pedestrians, we introduce an evidential-based data fusion process that exploits redundant information coming from one or two sensors: intra-sensor fusion uses spatiotemporal characteristics of the measurements and inter-sensor fusion uses redundancy between the two sensors. As part of the EU funded TRACKSS project on cooperative advanced sensors for road traffic applications, real data have been collected on an urban intersection equipped with two cameras. The results obtained show that the data fusion process enhances the quality of occupancy patterns obtained and leads to high detection rates of pedestrian crossings with multi-purpose sensors in operational conditions, especially when a secondary sensor is available.     

一种新的船舶柴油机模糊故障诊断方法

为进一步提高船舶柴油机故障诊断的可靠性,将一种基于模糊信息多级融合的故障诊断方法应用到船舶柴油机的故障诊断中,该方法将各级诊断数据充分融合后再进行船舶柴油机的故障诊断,应用结果表明该方法准确有效,不但在正常情况下作出了准确的故障诊断,而且在局部检测传感器失灵发生误检的情况下亦能避免船舶柴油机故障诊断的误判,为提高船舶柴油机故障诊断的可靠性提供了有益的借鉴.     

多传感器信息融合仿真平台的设计与实现

在多传感器信息融合系统的开发中,需要一个灵活可靠的仿真平台来对各种传感器模型、关联算法和融合算法进行调试评估。多传感器信息融合仿真平台经过模块化设计,面向对象方法编程,使得其具有开放性,易于扩展等特点。并给出一个该仿真平台的运行实例,使该仿真平台的可靠性、实用性等得到验证。     

MapX在数据融合算法可视化系统中的应用

融合算法研究一直是数据融合方向研究的重点,由于传感器信息量大,相关实验数据复杂,对数据的表示和分析比较困难,研制直观的、图形化的融合处理可视化系统迫在眉睫。根据数据融合的要求,分析数据融合可视化系统的特点,在此基础上构建多图层的融合可视化系统框架模型,并利用Visual C＋＋开发环境结合通用GIS组件MapX研制出满足实际需要的软件,取得了良好的效果。     

船舶电场信号的数据融合

船舶电场只能减少,不能消除。文章介绍了船舶电场信号的提取方法,并分别通过小波和时频分析方法对所得信号进行降噪。利用数据融合技术对信号进行综合处理。     

数据融合技术在海图作业标绘台的应用

论文将数据融合技术用于海图作业标绘台的设计,解决了海图作业标绘台实时标绘本舰和多目标航迹从而进行态势估计。文中对海图作业标绘台发展状况进行了简单介绍,并介绍了数据融合技术的应用背景、常用算法及研究内容．设计了相关模型,并具体论述了多目标检测和多目标跟踪技术在海图作业标绘台中应用。     

基于贝叶斯建模的轨道占用识别方法

识别列车所在轨道,是列车运行控制系统必不可少的功能. 提出一种数字轨道地图辅助的基于贝叶斯建模的轨道占用识别方法. 首先,在考虑全球导航卫星系统(Global Navigation Satellite System, GNSS)与方向相关测量误差的基础上进行地图匹配,采用卡尔曼滤波算法处理速度测量值,通过加权求和的方式对GNSS与速度信息进行融合,获得列车沿轨道方向的一维位置;其次,对列车位置假设进行贝叶斯建模,计算在给定GNSS与速度测量的前提下所有可能的位置假设的概率;最后,与设置的假设概率阈值进行比较,对不同的识别结果进行分类. 实验结果表明,基于贝叶斯建模的轨道占用识别法能够减少剔除小概率假设所需的距离,与垂直投影法相比,该方法可以对列车所在轨道做出更确定的判断.     

综合交通信息系统信息平台的构建

分析了城市变通系统的现状,研究综合变通信息系统的整合,提出并论述了系统整合中的共用信息平台的建设厦智能运输系统体系结构建议方案。