基于DSP的车载GPS/DR/MM组合导航仪

对于城市车辆GPS导航来说，其很大的一个缺点是跟踪卫星的信号常常由于建筑物，隧道及树木等的遮挡而使GPS的定位精度大大降低，甚至无法进行正常的导航和定位。为此，作者研制，开发了基于DSP的车载GPS／DR／MM组合导航仪，本文详细介绍导航仪的硬件组成和软件编程，给出导航仪的组合方法，并进行跑车试验，试验结果表明导航仪具有良好的导航与定位性能，解决了单纯GPS系统无法连续定位和行驶中的车辆“上房”问题。     

面向动态导航的城市路网实时交通信息服务系统研究

结合国内外的研究现状，基于实时GPS数据和高架线圈检测数据的数据融合处理技术，给出了面向动态导航的实时交通信息服务系统的总体框架，实现了城市路网的实时交通流状态估计；通过构建实时交通信息系统，借助无线通讯方式为车辆提供实时交通信息和动态路径规划服务，实现了在动态路径规划基础上的车辆动态导航。     

面向ITS的车辆导航与定位技术

章简述了车辆自动定位导航系统的基本结构及关键技术，着重探讨了提高车辆定位精度及可靠性的组合导航措施，并讨论了GPS／DR／MM组合导航系统的信息综合方法及对数字地图的要求。     

基于V2X通信网络的车辆协同定位增强方法

鉴于车辆位置精度对车联网应用的重要性和传统的卫星定位系统在城市"高楼峡谷"或隧道等弱卫星信号覆盖区域失效或定位精度不足等问题,本文中研究了基于V2X通信网络的车辆定位方法,研发了专用短程通信和多模式通信网络定位终端,完成了终端的性能测试和应用部署,设计了基于多模型滤波算法的车辆协作定位增强方法。结果显示,与传统GPS定位相比,本文方法能提升定位精度近40%。     

基于模糊逻辑的导航定位数据校正算法

为了提高GPS/DR组合定位系统的定位精度,通常采用地图匹配算法来修正定位误差.文中采用了一种基于模糊逻辑的导航定位数据校正算法,对经联合卡尔曼滤波输出的GPS/DR的定位数据进行校正.通过Matlab仿真实验,结果表明,该算法能有效地减小误差,提高组合定位系统的定位精度,改善其对航线跟踪的质量.     

基于GPS/DR与GIS集成的城市车辆实时监控系统的设计与工程应用

结合香港某种车辆调派系统和南京市某银行运钞车实时监控系统的工程应用，给出基于GPS/DR／GIS集成车辆导航系统的设计与组成，以及系统各个模块之间的相互关系；研究并提出适合车辆导航数字地图的数据组织，大地坐标系与WGS84坐标系、WGS84坐标系与独立坐标系间的相互转换关系；详细介绍本系统在不同工程应用中各自实现的功能。本系统在香港实地跑车实验中，在无GPS信号的情况下，运用DR／MM定位模块连续定位15min后，测得其定位精度小于5m。     

多媒体GPS车辆监控导航管理系统研究

简述GPS技术的原理，综合分析多媒体GPS车辆监控导航管理系统的研究背景以及国内外GPS车辆监控导航等理系统的研究现状，在此基础之上，着重研究符合我国国情的车辆导航系统，重点阐述了多媒体GPS车辆监控导航管理系统的设计方法，进而拟定系统软件开发框架和各组成部分的性质功能。详细介绍图文信息完善结合的软件工具MapInfo及其桌面地图信息系统的鲜明特点。     

驾驶机器人车辆动态制动力矩补偿

为了减小长期自动驾驶过程中制动性能下降带来的影响,提出了一种驾驶机器人车辆动态制动力矩补偿方法。首先建立了以车速和制动踏板力为输入,制动力矩为输出的驾驶机器人车辆制动性能离线自学习模型。然后考虑到驾驶机器人车辆长期自动驾驶导致离线自学习模型可靠性下降,建立了以车速和制动踏板力为输入,制动力矩为输出的扩展自回归在线辨识模型,并采用模糊变遗忘因子递推最小二乘法进行参数辨识。模糊变遗忘因子递推最小二乘法通过引入遗忘因子的方式,对数据施加时变加权系数,以避免出现数据增长导致的数据饱和现象。模糊变遗忘因子控制器以制动力矩辨识误差为输入,经模糊规则推理实时输出合适的遗忘因子进行参数辨识,能够有效均衡驾驶机器人车辆制动性能参数辨识的稳定性与收敛速度。驾驶机器人车辆自动驾驶过程中,根据当前车速与目标车速的大小计算出所需的制动力矩,加上反馈回来的制动力矩误差,并结合当前时刻的车速,利用制动性能离线自学习模型与机械腿逆向运动学模型实时计算出制动电机输出位移量,实现对驾驶机器人车辆制动力矩的在线补偿。仿真与试验结果表明:利用所提出的方法对车辆动态制动力矩进行辨识时,通过调节遗忘因子,辨识结果能够快速收敛且辨识误差较小。在此基础上,控制驾驶机器人车辆进行纵向车速跟踪时,能够有效减小制动性能下降造成的影响,保证控制车速跟踪误差在±1km·h-1之内。     

基于GPS的车载定位导航接收系统的设计

GPS技术能快速提供精确三维坐标以及其他相关信息,具有高精度、自动化等显著特点,广泛应用于军事、民用交通导航、测量等重要领域。其中,车载定位导航系统应用最为广泛,利用GPS导航技术,结合地理信息系统、多媒体等其他技术对车体所在位置进行地理定位,为车辆实时提供行车导航信息。将GPS技术应用于车载导航系统时,软、硬件的设计尤为重要,关系着车载导航系统能否准确、高效的提供实时导航信息。结合GPS导航技术,文章对软、硬件分别设计研究,开发出一套准确、高效的车载定位导航接收系统。     

高精度车载导航定位系统的研究

通过对车载导航系统结构、功能的分析，对车辆的高精度导航定位进行了研究。在常见的卫星导航（GPS）、航位推算（DR）等导航定位的基础上，进一步研究并实现了地图匹配算法，克服了GPS信号受阻时定位间断或失效的缺点，避免了航位推算定位误差随时间的积累。通过大量的跑车试验表明，基于GPS／DR／MM的车辆导航系统，可以高精度地实现车辆定位，进而可以实现地理信息的查询、交通诱导等功能。具有重要的实际应用价值。     

Using GPS with a model-based estimator to estimate critical vehicle states

This paper demonstrates a method to estimate the vehicle states sideslip, yaw rate, and heading using GPS and yaw rate gyroscope measurements in a model-based estimator. The model-based estimator using GPS measurements provides accurate and observable estimates of sideslip, yaw rate, and heading even if the vehicle model is in neutral steer or if the gyro fails. This method also reduces estimation errors introduced by gyroscope errors such as the gyro bias and gyro scale factor. The GPS and Inertial Navigation System measurements are combined using a Kalman filter to generate estimates of the vehicle states. The residuals of the Kalman filter provide insight to determine if the estimator model is correct and therefore providing accurate state estimates. Additionally, a method to predict the estimation error due to errors in the estimator model is presented. The algorithms are tested in simulation with a correct and incorrect model as well as with sensor errors. Finally, the estimation scheme is tested with experimental data using a 2000 Chevrolet Blazer to further validate the algorithms.     

车辆导航系统中基于三维电子地图的单GPS车道级定位方法研究

在对三维电子地图建模的基础上,提出了一种基于单GPS的虚拟差分、高程辅助定位和碰撞检测误差消除相结合的车道级车辆定位方法.该方法将车辆定位分为两次地图匹配过程,即首次通过虚拟差分技术和高程辅助定位技术对GPS定位参数进行修正,提高定位精度,然后在三维电子地图中结合碰撞检测技术实现车道级匹配.通过实地DGPS跑车对比实验,证明该方法完全能够满足车道级路径引导的要求.     

A hybrid dead reckoning error correction scheme based on extended Kalman filter and map matching for vehicle self-localization

In this research, a hybrid dead reckoning error correction scheme is developed based on extended Kalman filter (EKF) and map matching (MM) to improve the positioning accuracy for vehicle self-localization. The developed method aims at obtaining accurate positions when the GPS signals are occasionally unavailable or weakened. First, the heading data collected from an odometer and an optical fiber gyroscope are integrated by an EKF to reduce the random errors in dead reckoning. Then a modified topological MM algorithm is developed to reduce the systematic errors in dead reckoning. In this work, both cross-track errors and along-track errors are considered to improve positioning accuracy of MM. The errors are finally corrected using the results achieved from both the dead reckoning and the MM when the driving distance of a vehicle exceeds a predefined length or the vehicle turns in an intersection. Experiments have been conducted to evaluate the developed method and the results show that the maximum error and average error of dead reckoning can be respectively reduced to 15.4?m and 5.2?m during the experiment with total distance of 43?km. This positioning accuracy is even better than the accuracy of the low-cost GPSs which are usually at the order of 15–20?m (95%). The developed method is effective to achieve the positions of the vehicle when the GPS signals are occasionally unavailable or weakened.     

GPS多路径效应原理及数据后处理方法研究

该文对GPS定位测量中多路径效应误差产生的原理进行了分析,总结了多路径效应的特点及各类消除方法。对消除多路径效应的数据后处理方法进行研究,指出了基于EMD分解方法的优势及特点。     

车载导航系统陀螺的自适应校正方法

分析了低成本压电振动陀螺误差及其影响因素,在实验的基础上得出采用温度补偿陀螺误差的可行性。建立了联合卡尔曼滤波方程融合GPS和INS信息,估计定位信息和陀螺误差。提出车载GPS/INS组合导航系统中陀螺零漂误差和标度因子误差的校正过程启动条件,当条件满足时,以估计的陀螺误差为输入,采用温度误差校正表学习算法对陀螺误差模型进行训练。用道路实验数据对提出的陀螺校正算法进行验证,结果表明该算法精度高、收敛快、可操作性好。     

车辆GPS监控导航系统研究

综合分析了GPS车辆监控导航管理系统的研究背景以及国内外GPS车辆监控导航管理系统的研究现状.在此基础之上,以MapInfo为研究平台,选用VC++作为系统的开发工具,研制了符合我国国情的车辆导航系统.文中重点阐述了GPS车辆监控导航管理系统的设计方法,进而拟定了系统软件开发框架和各组成部分的性质功能.并以武汉市一环线为案例进行了模拟研究,结果表明所研制的GPS车辆监控导航管理系统能够有效的实施车辆的跟踪导航.     

Lateral controller design for an unmanned vehicle via Kalman filtering

This paper proposes a lateral control system for an unmanned vehicle that is designed to improve the responsiveness of the system with the use of a PD control. The vehicle heading error can be stabilized, and the transient response characteristics can be improved using the proposed controller. A mathematical model of the vehicle dynamics using two degrees of freedom was developed for the controller design. The waypoint tracking method for autonomous navigation was tested with incorporation of the Point-to-Point algorithm with position and heading measurements received from GPS receivers via Kalman filtering. The performance of the designed controller was verified through experiments with a real vehicle.     

Integration of GPS and monocular vision for land vehicle navigation in urban area

For land vehicle navigation in urban area, Global Positioning System (GPS) receivers often suffer from the lack of positioning accuracy, availability, and continuity due to insufficient number of visible satellites and multipath errors. To mitigate this problem, this paper proposes an efficient hybrid positioning method combining a single frequency GPS receiver and a monocular vision sensor. The proposed method is advantageous in that it requires only low-cost hardware and no external map aiding. Compared with existing vision-based methods, the proposed method directly measures absolute heading angle based on the images of straight road segments. For the reason, the proposed method is resilient to multipath errors. The performance of the proposed method is evaluated by the experiments with field-collected real measurements; one with good satellite visibility and the others with poor satellite visibility. Comparison with existing positioning methods demonstrates the feasibility of the proposed method in urban area.     

The Use of GPS Based Velocity Measurements for Measurement of Sideslip and Wheel Slip

Summary This paper details a novel method for measuring three key vehicle states – wheel slip, body sideslip angle, and tire sideslip angle – using GPS velocity information in conjunction with other sensors. Based on initial noise data obtained from the system components, a prediction of the accuracy of the angle measurements is obtained. These results demonstrate that the errors due to stochastic noise in the GPS signal are below one degree for meaningful vehicle speeds and approach a tenth of a degree at highway speeds. Hence the limiting factor for measuring these states is not the GPS receiver, but the manner in which other implementation issues – such as bias elimination, off-axis dynamics and dead-reckoning during loss of satellite visibility – are handled. Subsequent experiments validate both the error analysis and the methodology for obtaining the measurements. The experimental results for this preliminary implementation of GPS-based state estimation compare favorably to theoretical predictions, suggesting that this technique has potential for future implementation in vehicle diagnostic and, ultimately, safety systems.