基于线性分割的动态称重传感器阵列优化方法

基于部分承载模型的动态称重系统,能够满足车辆在高速行驶状态下的精确动态称重.然而当车轮静止于传感器上再次启动时,受其模型所限误差较大.针对这一问题以路面交互模型为基础,融合线性分割方法,提出一种基于线性分割的部分承载动态称重模型,并开发了4排并列式动态称重系统.经过实验对比验证,使用新模型构成的动态称重系统能够在车辆变速以及起停状态下整车误差仅为±2%,为重车限重管理系统、非现场治超等应用提供了一定的支持.     

考虑行车能见度状况的车辆队列协作控制

本文考虑了能见度状况影响下的车辆队列协作控制问题.针对车辆行驶中可能出现的3种(正常、低以及超低)能见度状况,分析了其对距离传感器测量输出的影响,并建立了具有切换结构的车辆控制模型.基于平均驻留时间技术以及分段Lyapunov函数方法,在不同能见度状况下,得到了能够保证车辆列队跟踪误差稳定的车辆控制器存在条件以及控制器增益求解方法.通过对车辆控制器增加限制条件,得到了能够保证队列稳定性要求以及实现零稳态距离跟踪误差的车辆协作控制算法.通过MATLAB仿真实验以及Ardunio智能小车实验,验证了本文所提出的算法的有效性以及实用性.     

基于路径损耗模型参数实时估计的无线定位方法

定位技术在无线传感器网络中越来越引起人们的重视.针对传统的测距算法中,使用固定路径损耗模型会造成较大测距误差的问题,提出了一种基于RSSI的动态调整信号路径损耗模型参数的协作定位方法.该方法首先根据RSSI确定定位节点所在的最小子区域,然后通过该区域内已知参考节点间的相互合作估算出该子区域的路径损耗模型参数,再根据得到的路径损耗模型实现准确测距,从而减小定位误差.仿真和实验结果验证了该方法提高了定位精度.     

在分布式VR系统中推算定位

文中基于行为模型ＳＣＰ提出一种分布式ＶＲ系统的推算定位方法。这种方法使用能够反映较长时间内动态实体对象行为状态进行推算定位,从而有效克服了已有推算定位方法行为误差较大、速度高时网络数据量大的缺点。逐渐逼近法的引入又使得平衡性较好。同已有两种推算定位文学实验比较的结果表明,这种方法具有状态失真度小、网络数据量较小的优点。     

一种抗动态运动干扰的雷达车辆测距算法仿真

研究车辆行驶过程中的准确测距问题.车辆在行驶的过程中,行驶速度存在较强的突变性,且很难运用动态模型进行描述,而传统的利用计算机视觉信号的采样频率采样的方法也很难保证较为完全地采集这种信号突变,最终动态的信号变换只能导致测距结果失真,降低了车辆测距的准确性.为了避免上述缺陷,提出了一种抗击动态运动干扰的雷达车辆测距算法.采集车辆测距调频信号,利用非线性滤波方法对其进行去噪处理,得到准确的中频信号.通过计算中频信号动态发射频率消除动态误差,从而实现车辆测距.实验结果表明,这种算法能够有效提高车辆测距的准确性,取得了令人满意的效果.     

利用GPS进行车辆动态定位的自适应模型研究

提出一种GPS动态定位系统模型,并将其应用于车辆的导航定位系统,获得了明显效果.将GPS的误差等效为马尔柯夫过程,采用描述机动载体运动的"当前"统计模型,建立了一种利用GPS对车辆进行动态导航定位的滤波模型及自适应卡尔曼滤波算法.仿真结果表明,应用所提出的强跟踪动态定位模型和算法,与改进前相比车辆导航定位系统的精度、实用性均得到了明显提高.     

智能车双目视觉辅助GNSS定位方法研究

具有精确、稳定的定位结果以及合理的价格是未来的智能车辆导航系统的发展趋势。为了达到这个目标,人们建立了多种组合导航模型（GNSS/DR,GNSS/INS,GNSS/MM）。尽管这些模型已在多种不同环境中成功应用,但它们仍有许多缺陷,尤其是在全球卫星导航系统（GNSS）定位精度受到威胁的区域。研究了一种通过双目视觉利用路标的地理位置信息对GNSS定位精度进行局部改良的方法。随机霍夫变换用于路标检测,SIFT算法与K均值算法将用于路标的匹配识别。双目视差计算智能车与路标之间的向量,从而建立辅助定位模型计算车辆的位置。利用实验车在一处复杂环境区域进行实时数据采集,通过计算出的双目视觉定位误差与GNSS定位误差对比分析,验证了该方法在路标可见范围内对GNSS定位结果有明显改善。     

基于移动ad-hoc网络的车辆定位

当车辆不能通过GPS接收机获得自身位置信息时,就难以获得有用的交通提示风险服务.提出了一种新的基于车载移动ad-hoc网络车辆定位方法,该方法能够获取作为网络节点车辆的大致位置,并结合一种改进的警报信息优化传播算法,向即将处于危险或拥堵区域的不能通过GPS接收机定位的车辆发送警报信息.仿真实验表明只要车辆自组织网络中有40%的车辆可以获得GPS的定位信息,就可以将警报信息准确完整地送达处于网络中的所有车辆.当遇到浓雾天气、交通事故或者是其他拥塞时,该方法会防止车辆进一步拥堵并能提醒驾驶员防范危险.     

无线传感器网络LQI测距定位算法及其误差研究

无线传感器定位算法对无线传感器的工程应用具有重要的意义.针对基于LQ I测距的无线传感器网络,研究了测距模型的建立并对比分析了最小二乘定位算法和质心定位算法,相关仿真研究结果表明:该两种定位算法的精度与测距误差的性质具有鲜明的规律性,当测距误差正向分布时,最小二乘定位比质心算法精度高;当测距误差正负双向分布时,质心算法比最小二乘定位精度高.最后,文中通过搭建的Z igBee硬件平台实验验证了仿真的结果.本文从测距误差模型入手对无线传感器定位算法进行研究,仿真结果与实验验证相一致,有利于W SN的推广应用.     

基于无线传感器网络的室内定位技术的研究

对WSN中基于测距技术的定位方法进行了研究,针对室内环境易对信号造成干扰且硬件存在差异的情况,采用为每个参考节点设置其测距模型的方案.对利用线性定位和极大似然估计两种定位算法分别进行了分析,通过实验测试定位系统中测距模型的测距误差以及两种定位算法的定位误差,依据实验结果提出了综合运用两种定位算法的策略.     

2D map-building and localization in outdoor environments

Determining the pose (position and orientation) of a vehicle at any time is termed localization and is of paramount importance in achieving reliable and robust autonomous navigation. Knowing the pose it is possible to achieve high level tasks such as path planning. A new map-based algorithm for the localization of vehicles operating in harsh outdoor environments is presented in this article. A map building algorithm using observations from a scanning laser rangefinder is developed for building a polyline map that adequately captures the geometry of the environment. Using this map, the Iterative Closest Point (ICP) algorithm is employed for matching laser range images from the rangefinder to the polyline map. Once correspondences are established, an Extended Kalman Filter (EKF) algorithm provides reliable vehicle state estimates using a nonlinear observation model based on the vertices of the polyline map. Data gathered during field trials in an outdoor environment is used to test the efficiency of the proposed ICP-EKF algorithm in achieving the localization of a four-wheel drive (4WD) vehicle. © 2005 Wiley Periodicals, Inc.     

Modeling the impact of observation conditions on localization systems

In various applications, sensor fusion has demonstrated success as means to enhance a system performance in perceiving its environment. By combing observations of different sensors, the system is able to achieve improved sensing accuracy, and potentially, expanded sensing capabilities. However, the observation conditions in the surrounding of any multi-sensor system have a considerable impact on the performance of the system. This impact can be hard to mitigate if the observation conditions are stochastic in nature. Therefore, for any sensor fusion strategy to achieve reliable and robust performance it must possess a capability to assess the quality of the observation conditions in its surrounding, and ultimately, the quality of its decisions, as a function of these conditions. One typical application where the impact of the observation conditions can cause sever deterioration of the sensing performance is vehicle localization. It is typical in this application that location measurements obtained from multiple sensors (e.g., GPS, Vision, Inertial, etc.) are combined together to compute accurate vehicle location. However, such improved accuracy can only be attained under nominal observation conditions. Therefore, real-time awareness of the observation conditions around the vehicle position is pivotal for the multi-sensor system to achieve effective fusion performance.In this paper, a Markovian model is proposed to capture the impact of observation conditions on a sensor’s localization performance and to consequently determine a reliability index with respect to the localization accuracy claimed by the sensor.The proposed model is implemented on two localization techniques: single-sensor localization and multi-sensor localization. A number of experiments are conducted to determine the different levels of localization accuracy that can be achieved by each technique under a wide range of observation conditions. The proposed reliability model is tested in a variety of real-life and simulated observation conditions scenarios. It is evident from the experimental results that the proposed model is able to estimate the reliability of location estimates produced by either one of the localization techniques. The paper discusses how such reliability model can benefit multi-sensor systems.     

Embedded omni-vision navigator based on multi-object tracking

This paper presents an embedded omni-vision navigation system which involves landmark recognition, multi-object tracking, and vehicle localization. A new tracking algorithm, the feature matching embedded particle filter, is proposed. Landmark recognition is used to provide the front-end targets. A global localization method for omni-vision based on coordinate transformation is also proposed. The digital signal processor (DSP) provides a hardware platform for on-board tracker. Dynamic navigator employs DSP tracker to follow the landmarks in real time during the arbitrary movement of the vehicle and computes the position for localization based on time sequence images analysis. Experimental results demonstrated that the navigator can efficiently offer the vehicle guidance.     

One-point-plus for 5-DOF localization of vehicle-mounted omnidirectional camera in long-range motion

This paper proposes a method for localization of vehicle using one point plus an edge matching region of monocular vision in wide urban environments. The five degree of freedom (5-DoF) localization estimated by monocular omnidirectional camera improves the planar motion assumption in most of conventional researches. In recent year, the car-like motion model with planar motion is often investigated to reduce the requirements of correspondence until one point. However, in the real application of long-range motion in outdoor scene, the motion may not satisfy this condition. This leads to the inaccurate vehicle localization. In this proposed method, the car-like model is also utilized for 5-DoF localization however the requirements of correspondence are reduced to only one point plus an edge matching region which is much simpler than the conventional 5-point RANSAC. The cumulative errors of visual odometry are excluded by using global positioning system (GPS) correction under equation of maximum likelihood estimation in Extended Kalman Filter (EKF) frame work. The real application in hills and mountainous regions demonstrates the accuracy of vehicle localization using this proposed method.     

基于多层级联视觉显著性模型的肇事车辆锁定方法

肇事车辆的锁定是智能交通系统中一个十分重要的问题,因此针对肇事车辆的锁定,提出了一种基于多层级联视觉注意模型的肇事车辆匹配方法.在模型的每一层中,基于传统视觉注意模型的思想,通过生成显著图的方式提取车辆的一个显著性特征,如颜色、车标,并将其与肇事车辆进行匹配,过滤掉特征不相似的车辆,经过多次显著性特征提取和匹配,最终获得唯一的肇事车辆.实验结果表明,该模型可以准确地从车辆数据库中锁定肇事车辆,且对光照变化和噪声有较强的鲁棒性.     

Vehicle Detection Using Spatial Relationship GMM for Complex Urban Surveillance in Daytime and Nighttime

Traffic surveillance is an important issue in intelligent transportation systems. Efficient and accurate vehicle detection is one challenging problem for complex urban traffic surveillance. As such, this paper proposes a new vehicle detection method using spatial relationship GMM for daytime and nighttime based on a high-resolution camera. First, the vehicle is treated as an object composed of multiple components, including the license plate, rear lamps and headlights. These components are localized using their distinctive color, texture, and region feature. Deriving plate color converting model, plate hypothesis score calculation and cascade plate refining were accomplished for license plate localization. Multi-threshold segmentation and connected component analysis are accomplished for rear lamps localization. Frame difference and geometric features similarity analysis are accomplished for headlights localization. After that, the detected components are taken to construct the spatial relationship using GMM. Finally, similar probability measures of the model and the GMM, including GMM of plate and rear lamp, GMM of both rear lamps and GMM of both headlights are adopted to localize vehicle. Experiments in practical urban scenarios are carried out under daytime and nighttime. It can be shown that our method can adapt to the partial occlusion and various lighting conditions well, meanwhile it has a fast detection speed.     

洋流影响下基于运动矢径的AUV协同定位方法

针对水下自主航行器（AUV）协同定位受水下未知定常洋流影响的问题,给出一种洋流影响下基于运动矢径的AUV协同定位方法.利用AUV的运动学方程和基于运动矢径的量测方程,建立AUV的导航模型;通过扩展的卡尔曼滤波,设计了协同定位滤波算法;利用该算法对洋流速度进行估计,以补偿AUV定位误差.仿真结果表明,该算法能有效估计未知定常洋流速度的大小,并对AUV定位误差进行实时补偿,显著提高了AUV的定位精度.     

基于凸优化算法的无人水下航行器协同定位

In this paper, a cooperative localization algorithm for autonomous underwater vehicles (AUVs) is proposed. A ``parallel" model is adopted to describe the cooperative localization problem instead of the traditional ``leader-follower" model, and a linear programming associated with convex optimization method is used to deal with the problem. After an unknown-but-bounded model for sensor noise is assumed, bearing and range measurements can be modeled as linear constraints on the configuration space of the AUVs. Merging these constraints induces a convex polyhedron representing the set of all configurations consistent with the sensor measurements. Estimates for the uncertainty in the position of a single AUV or the relative positions of two or more nodes can then be obtained by projecting this polyhedron onto appropriate subspaces of the configuration space. Two different optimization algorithms are given to recover the uncertainty region according to the number of the AUVs. Simulation results are presented for a typical localization example of the AUV formation. The results show that our positioning method offers a good localization accuracy, although a small number of low-cost sensors are needed for each vehicle, and this validates that it is an economical and practical positioning approach compared with the traditional approach.     

基于纹理特征的车牌定位方法

为了提高车辆牌照定位成功的概率以及定位精度,提出了一种基于纹理特征,采用自适应二值化的车牌定位方法。该方法首先利用小波分析对图像进行预处理,提取车牌图像字符区域的纵向纹理特征,然后利用边缘检测算子对图像纹理特征进行二次提取,再进行自适应二值化。该方法克服了直接对小波分析后图像进行二值化时,阈值选取非常困难的缺陷。实验结果表明,该方法能够达到提取有效车牌图像的目的,适用于各种复杂条件下拍摄的车牌图像定位。