基于GPS/DR的GPS滞后时间自适应检测算法

针对GPS输出信息的随机延迟特性,基于航位推算(DR)系统惯性器件的实时性与短时高精度,提出了航向差和速度自适应GPS滞后时间检测方法,在此基础上,对GPS和DR数据进行数据对准,提出了自适应GPS滞后时间处理算法。在GPS/DR实际系统中的应用结果表明,本算法能够准确检测出GPS的滞后时间,提高车辆的定位精度。     

无人机高精度容错高度测量系统设计

基于大气数据计算机、全球定位系统(GPS)和线加速度计,给出了一种高精度和高可靠性的高度确定算法。根据运动学和传感器测量模型建立了高度测量系统的状态方程和测量方程,利用Kalman滤波方法得到了高度估计,并根据测量信息的冗余设计了系统的故障检测和隔离算法。仿真结果表明:该系统的高度测量具有较高的精度,同时有着较好的可靠性。     

滚转条件下单天线GPS跟踪算法设计

提出了一种滚转条件下的单天线GPS跟踪算法。该算法能够在较低转速下,对间断的GPS信号进行跟踪,同时能够准确地计算出弹丸的滚转速度。该方法首先对单天线滚转条件下的接收信号进行分析与建模。然后,基于滚转对接收到的卫星信号的幅度调制特性的影响,利用旋转时信号幅度的对称性进行解旋,从而得到弹丸的滚动速率和角度。实际测试结果表明,利用信号的I/Q幅值信息,可以实时计算滚转弹丸的滚动速率和滚动角度,并且计算精度可以满足大多数实际应用的要求。     

利用Kinect的人体动作视觉感知算法

提出了一种面向类人机器人的人体动作视觉感知算法,提高了利用Kinect作为视觉输入设备捕捉到的人体动作数据的精度.首先,通过逆运动学方程将捕捉到的关节位移信息转换成角度信息.然后,以角速度和角加速度的变化为依据,将长时间的运动自动分割成独立片段,并用相关向量机原理估计出高精度的角度轨迹.最后,用角度轨迹的空间相似性、时间相似性、平滑度等指标对该算法进行了评估,并在NAO机器人平台上对算法处理后的动作进行了实验验证.实验结果表明,该算法有效提高了动作感知的时空相似性和轨迹平滑度,为高精度的动作模仿奠定了基础.     

一种GPS数据记录仪的设计实现

GPS定位广泛应用在个各行各业的定位系统中,针对一款高精度的GPS接收机,进行了一种实时GPS数据记录仪的系统设计;系统设计以C8051F020为主控处理器,通过控制程序将高精度差分GPS OEM模块接收的定位数据实时存储在CF卡中,主要包括GPS数据接收机配置和CF卡存储设计;同时,系统设计了基于Windows操作系统的专用上位机软件来进行扇区的读数和数据导出,其功能包括扇区区间的选择,扇区数据的导出,磁盘格式化等;通过完整的硬件和软件设计,在可供用户二次开发的GPS OEM板上设计实现了一种高精度的可配置的实时GPS数据记录仪;设计的GPS数据记录仪具有小巧,轻便,可稳定工作于恶劣环境等特点,并且可以满足单点和差分GPS数据的记录;通过功能试验和验证,该系统工作稳定且可靠性高。     

近距离高精度汽车定点定位通信系统

针对汽车GPS通信系统在近距离定位上的精度问题,设计了一种近距离高精度定位辅助系统,并实现了周围汽车高效实时的通信。该系统利用超声波测距技术获得汽车之间的距离,并计算相对角度。通过有效的通信初始化算法与动态更新算法,将此数据与设备通信地址进行绑定,生成完整的节点信息。对节点信息进行处理,实时构建汽车周围环境的二维平面图,从而实现汽车近距离选择性通信的目的。     

基于GPS轨迹的盲用导航算法与实现

利用在Windows CE嵌入式平台下接收到的全球定位系统(GPS)轨迹数据,提出了一种适用于盲人以及视障人士的室外GPS导航算法。通过对已知GPS轨迹数据的分析,判断出轨迹上的拐点位置以及拐弯方向,在实际导航时采用通过语音播报的方式向盲人提供实时导航信息。实验测试结果表明,该算法能够有效筛选出GPS轨迹中的拐点,并为用户播报有效实用的导航信息,指导其安全准确地到达目的地。     

基于信息融合的LiFePO4动力电池组SOC估计

针对复杂工况下LiFePO4 动力电池组state-of-charge(SOC) 估计不准确的问题, 基于信息融合技术提出一种 SOC 估计信息融合架构和多模型切换估计(MMSE) 算法. 该算法首先对充放电过程进行特征提取和模式分类, 针对 特定的模式进行模型优化; 然后在系统运行时根据特征匹配结果切换估计模型, 实现优化估计; 最后通过纯电动客车 实际运行数据的仿真实验验证了所提出MMSE算法的可行性和有效性.     

一种基于车联网架构的宽视野图像信息系统

基于无线物联网架构,提出了一种可实现车路信息实时采集、共享的宽视野图像信息系统。该系统应用GPS全球定位系统采集位置信息,使用图像传感器获得车辆周围图像;通过无线车辆网络将数据汇总;最后通过图像拼接算法构建宽视野图像信息。针对系统中的核心部分——构建宽视野图像信息,提出了一种基于SURF(speeded up robust features)图像特征的图像拼接算法。实验证明,该算法时间复杂度低,拼接效果良好,且系统具有良好的扩展性,可用于进一步开发车联网的智能应用。     

一种水雷磁引信横向动作区域计算方法研究

围绕水雷磁引信中动作区域性控制问题,探讨一种舰船磁场横向距离计算方法.从舰船磁场矢量特性角度出发,重点对算法的计算原理和计算误差进行深入分析,指出该方法的实际使用时应考虑的影响因素.最后通过仿真实验,总结了算法的适用规律,为水雷引信控制提供有益的参考.     

用MAX7000A实现多普勒信号数字信号处理的分析与线路设计

本文以连续波多普勒无线电引信为研究背景,建立目标信号数学模型,针对目前炸高控制方法及存在问题,提出了一种新的多普勒脉宽技术测高方法,在此基础上增加一个近表面炸的性能。并介绍了采用可编程逻辑器件实现的方法以及计算机仿真。实践证明,对Ｄｏｐｐｌｅｒ信号的数字信息处理,用一片ＣＰＬＤ实现,提高引信信号处理的速度,并极大提高了系统的可靠性。     

GPS/INS组合导航数据同步时标的计算方法研究

以GPS/INS组合导航系统为应用背景,针对GPS数据和惯性数据中时间同步的问题,提出了基于横摆角速度匹配、实时计算GPS延迟的方法,用于修正惯性数据与GPS的同步时标,提高了组合导航和车辆状态检测的精度。实验结果表明,计算得到的GPS延迟能够起到较好的修正效果。该方法仅利用导航系统数据,无需汽车内部参数和采集信号,具有自主性,有利于应用于外置检测和导航系统。     

旋转状态下GPS中频信号仿真研究

GPS技术在导弹制导、新型引信等高动态自旋载体中的应用时,天线的旋转导致接收到的信号被幅度调制,且载波多普勒频移变化有着一定的复杂性.考虑到实际中真实的高动态旋转状态下的信号难以采集到,提出了对信号进行数学建模后利用软件生成的方法.以安装在旋转载体上的单个GPS天线接收到的信号为研究对象,通过对载体的运动模型和中频信号参数的研究,给出了旋转状态下GPS中频信号的生成模型,并在研究的基础上对信号进行了仿真实现.仿真结果对研究弹载,箭载等GPS接收机有重要的理论研究价值.     

基于姿态随动技术的直升机低高度测量

在飞行试验中悬停性能是直升机性能的重要试飞科目。该科目科研试飞鉴定要求有大量的样本数据(海拔、温度、重量、构形的组合)以获取比较准确的试验结论。其中,离地高度是悬停科目试飞与小速度科目试飞中的关键参数。直升机的低高度信息通常由气压式高度传感器、无线电高度表获取。由于受到地面效应、姿态、风速风向等诸多因素的干扰,从而无法保证高精度和可靠性。激光测距逐渐成为一种先进的测量手段应用于直升机低高度测量中。但是在具体实施过程中由于直升机机动飞行姿态变化导致激光高度测量的误差增大与失效。通过机载姿态跟随技术研究不仅克服了直升机机动状态下姿态变化引起的低高度测量误差与失效,而且可由机载综合采集系统将低高度数据融合,实时发送给地面站遥测接收与存储,可以极大扩充数据样本,从而获取比较准确的试验结论。     

低空无人机航空摄影高度自动测量方法研究

为控制低空无人机摄影高度,获得更加清晰的地理信息图像,需要对低空无人机摄影高度自动测量方法进行优化研究;当前方法主要利用射影几何知识的自动化标定方法实现低空无人机航空摄影高度的自动测量;该方法存在噪声影响严重,且测量误差较大的问题;为此,提出一种基于多传感器与卡尔曼滤波相结合的低空无人机航空摄影高度自动测量方法;该方法首先通过分析气压测量法计算各种气压因素对低空无人机航空摄影高度的影响,然后推导出大气对流层内气压随低空无人机航空摄影高度的变化;然后采用双GPS系统同时工作,对GPS、气压高度计和IMU测量获得的低空无人机航空摄影高度信号进行冗余备份;采用基于二阶多项式的修正方法对低空无人机航空摄影传感器输出值进行补偿和修正;根据动力学方程建立低空无人机航空摄影的动力学方程获得高度测量状态方程;最后采用卡尔曼滤波的线性最小方差估计准则对低空无人机航空摄影高度进行均方差估计计算,实现低空高度自动测量与校正。实验结果表明,所提方法具有精度高、收敛性好且滤波效果理想的优势。     

Improving positioning accuracy of vehicular navigation system during GPS outages utilizing ensemble learning algorithm

Recently, methods based on Artificial Intelligence (AI) have been widely used to improve positioning accuracy for land vehicle navigation by integrating the Global Positioning System (GPS) with the Strapdown Inertial Navigation System (SINS). In this paper, we propose the ensemble learning algorithm instead of traditional single neural network to overcome the limitations of complex and dynamic data cased by vehicle irregular movement. The ensemble learning algorithm (LSBoost or Bagging), similar to the neural network, can build the SINS/GPS position model based on current and some past samples of SINS velocity, attitude and IMU output information. The performance of the proposed algorithm has been experimentally verified using GPS and SINS data of different trajectories collected in some land vehicle navigation tests. The comparison results between the proposed model and traditional algorithms indicate that the proposed algorithm can improve the positioning accuracy for cases of SINS and specific GPS outages.     

Localization using GPS and VISION aided INS with an image database and a network of a ground-based reference station in outdoor environments

GPS/INS integrated systems do not guarantee robustness and accuracy of localization, because GPS has vulnerability to external disturbances. However, the overall performance and reliability of the system can be significantly improved by fusing multiple sensors with a different operating principle. In outdoor environments where GPS may be blocked, there are many features compared to the open space and these features can provide much information for UGV localization. Thus, this paper proposes an improved localization algorithm based on the hierarchical federation of three measurement layers, i.e., GPS, INS, and visual localization, to overcome the shortcomings of GPS/INS integrated systems. The proposed algorithm automatically switches the operation modes according to GPS status and a network of a ground-based reference station. A vocabulary tree with SURF is used in the visual localization method. In the data fusion of visual localization and INS, an asynchronous and time-delayed data fusion algorithm is presented because visual localization is always time-delayed compared with INS. By using DGPS to obtain the reference position under the dynamic conditions of the reference station, the restrictions of the conventional DGPS are overcome and all UGVs within WiBro communication range of the reference station can accurately estimate the position with a common GPS. The experiment results with a predefined path demonstrate enhancement of the robustness and accuracy of localization in outdoor environments.     

A 3D indoor positioning system based on low-cost MEMS sensors

A positioning system in the absence of GPS is important in establishing indoor directional guidance and localization. Inertial Measuring Units (IMUs) can be used to detect the movement of a pedestrian. In this paper, we present a three-dimensional (3D) indoor positioning system using foot mounted low cost Micro-Electro-Mechanical System (MEMS) sensors to locate the position and attitude of a person in 3D view, and to plot the path travelled by the person. The sensors include accelerometers, gyroscopes, and a barometer. The pedestrians motion information is collected by accelerometers and gyroscopes to achieve Pedestrian Dead-Reckoning (PDR) which is used to estimate the pedestrian’s rough position. A zero velocity update (ZUPT) algorithm is developed to detect the standing still moment. A Kalman filter is combined with the ZUPT to eliminate non-linear errors in order to obtain accurate positioning information of a pedestrian. The information collected by the barometer is integrated with the accelerometer data to detect the altitude changes and to obtain accurate height information. The main contribution of this research is that the approach proposed fuses barometer and accelerometer in Kalman filter to obtain accurate height information, which has improved the accuracy at x axis and y axis. The proposed system has been tested in several simulated scenarios and real environments. The distance errors are around 1%, and the positioning errors are less than 1% of the total travelled distance. Results indicate that the proposed system performs better than other similar systems using the same low-cost IMUs.     

A Visual Global Positioning System for Unmanned Aerial Vehicles Used in Photogrammetric Applications

The combination of photogrammetric aerial and terrestrial recording methods can provide new opportunities for photogrammetric applications. A UAV (Unmanned Aerial Vehicle), in our case a helicopter system, can cover both the aerial and quasi-terrestrial image acquisition methods. A UAV can be equipped with an on-board high resolution camera and a priori knowledge of the operating area where to perform photogrammetric tasks. In this general scenario our paper proposes vision-based techniques for localizing a UAV. Only natural landmarks provided by a feature tracking algorithm will be considered, without the help of visual beacons or landmarks with known positions. The novel idea is to perform global localization, position tracking and localization failure recovery (kidnapping) based only on visual matching between current view and available georeferenced satellite images. The matching is based on SIFT features and the system estimates the position of the UAV and its altitude on the base of the reference image. The vision system replaces the GPS signal combining position information from visual odometry and georeferenced imagery. Georeferenced satellite or aerial images must be available on-board beforehand or downloaded during the flight. The growing availability of high resolution satellite images (e.g., provided by Google Earth or other local information sources) makes this topic very interesting and timely. Experiments with both synthetic (i.e., taken from satellites or datasets and pre elaborated) and real world images have been performed to test the accuracy and the robustness of our method. Results show sufficient performance if compared with common GPS systems and give a good performance also in the altitude estimation, even if in this last case there are only preliminary results.     

GSM-R场强监测系统中地图匹配算法研究

通过对影响车载导航系统定位精度的各种GPS数据误差的分析,提出了在GSM-R(Global System for Mobile Communica-tions Railway,铁路专用移动通信系统)场强监测系统中采用基于匹配相似度的地图匹配算法,该算法以GPS定位数据、精确的电子地图及相关路段的历史数据为基础,通过匹配过程确定车辆在电子地图上的最大可能位置,从而弥补了传统匹配算法计算量大以及匹配不准确的不足;最后,在模拟平台上进行了一系列测试,测试结果表明,该算法中的车辆定位和地图匹配精度明显提高,约达到95%。