基于多传感器信息融合城市车辆导航定位

针对全球定位系统信号易受城市复杂的环境干扰,接收不可靠、航位推算只能进行短时间高精度导航、地图匹配仅可在拐弯处才能提供与地图精度相当的修正信息、城区车辆导航存在“盲区”问题,研制了采用蓝牙技术的新型路标传感器,研究了基于自适应联合卡尔曼滤波算法的多传感器信息融合技术。大量的试验研究表明：采用该信息融合技术后,城区车辆导航定位精度达到10m以内,符合高精度导航定位要求。     

基于GPS/INS的机载三维激光测控系统的设计

设计并实现了一种机载激光测控系统的软硬件方案,系统的GPS/INS传感平台,使得系统可以不依赖于姿态和位置的精确控制.应用高阶次的扩展Kalman滤波算法进行传感器数据的融合.利用数据融合得到的信息,通过解算获得激光三维点云数据.实验结果表明:设计的激光测量系统具有良好的可行性,所设计的高阶扩展Kalman融合算法对G...     

Optimal distributed Kalman filtering fusion for a linear dynamic system with cross-correlated noises

In this article, we study the distributed Kalman filtering fusion problem for a linear dynamic system with multiple sensors and cross-correlated noises. For the assumed linear dynamic system, based on the newly constructed measurements whose measurement noises are uncorrelated, we derive a distributed Kalman filtering fusion algorithm without feedback, and prove that it is an optimal distributed Kalman filtering fusion algorithm. Then, for the same linear dynamic system, also based on the newly constructed measurements, a distributed Kalman filtering fusion algorithm with feedback is proposed. A rigorous performance analysis is dedicated to the distributed fusion algorithm with feedback, which shows that the distributed fusion algorithm with feedback is also an optimal distributed Kalman filtering fusion algorithm; the P matrices are still the estimate error covariance matrices for local filters; the feedback does reduce the estimate error covariance of each local filter. Simulation results are provided to demonstrate the validity of the newly proposed fusion algorithms and the performance analysis.     

基于无迹卡尔曼滤波的被动多传感器融合跟踪

针对被动传感器观测的非线性问题,将无迹变换引入卡尔曼滤波算法中.进一步,针对其弱可观测性,采用多个被动传感器集中式融合跟踪策略,提出了基于无迹卡尔曼滤波的被动多传感器融合跟踪算法.以3个被动站跟踪为例进行仿真研究,结果表明所提出的算法可达到比经典的扩展卡尔曼滤波算法更高阶的跟踪精度.     

多传感器集中式增量卡尔曼滤波融合算法

在单个传感器的状态估计系统中,标准的增量卡尔曼滤波方法可以有效消除量测系统误差。对于多传感器情况,标准算法失效。针对该问题,提出了多传感器集中式增量卡尔曼滤波融合算法,即:增量卡尔曼滤波的扩维融合算法和增量卡尔曼滤波的序贯融合算法。在标准增量卡尔曼滤波算法的基础上,结合扩维融合和序贯融合的思想来实现多传感器数据的融合。实验结果表明,当存在量测系统误差时,提出的集中式融合算法与传统的集中式融合算法相比,提高了滤波精度,并且能够成功地消除量测系统误差。     

基于容积卡尔曼滤波的异类多传感器一致性融合算法

针对单一光频传感器获取目标特征信息存在的不一致性,提出一种基于容积卡尔曼滤波的异类多传感器一致性融合方法。首先,从原理上分析了激光、红外与雷达三类传感器量测信息的特征及其存在的差异,进而在容积卡尔曼滤波框架下,针对雷达、红外和激光探测等组成的典型目标侦测系统,结合一致性融合策略,通过对目标距离和方位信息融合处理改善目标状态估计精度。仿真结果表明：相对于传统的单传感器滤波方法,所提出的融合方法和策略具有较好的滤波性能。     

Adaptive tuning of a Kalman filter via fuzzy logic for an intelligent AUV navigation system

This paper describes the implementation of an intelligent navigation system, based on the integrated use of the global positioning system (GPS) and several inertial navigation system (INS) sensors, for autonomous underwater vehicle (AUV) applications. A simple Kalman filter (SKF) and an extended Kalman filter (EKF) are proposed to be used subsequently to fuse the data from the INS sensors and to integrate them with the GPS data. The paper highlights the use of fuzzy logic techniques to the adaptation of the initial statistical assumption of both the SKF and EKF caused by possible changes in sensor noise characteristics. This adaptive mechanism is considered to be necessary as the SKF and EKF can only maintain their stability and performance when the algorithms contain the true sensor noise characteristics. In addition, fault detection and signal recovery algorithms during the fusion process to enhance the reliability of the navigation systems are also discussed herein. The proposed algorithms are implemented to real experimental data obtained from a series of AUV trials conducted by running the low-cost Hammerhead AUV, developed by the University of Plymouth and Cranfield University.     

GPS/INS组合导航系统时间同步系统设计

为了说明高动态环境中时间同步对于组合导航系统的重要性,在Kalman滤波方程的基础上,推导了时间同步误差与Kalman滤波结果之间的定性关系.提出一种利用GPS接收机中1PPS(Pulse Per Second)信号作为同步标签的时间同步方法,将IMU中的数据加上精确的时间标签,从而达到时间同步的目的.全部时间同步功能由FPGA实现,利用Verilog HDL语言进行开发,整体硬件结构简单而且适用范围广.试验结果显示了这种时间同步设计可以明显减小滤波结果的估计误差,有效的提高了组合导航系统的定位精度.     

An adaptive fusion algorithm based on ANFIS for radar/infrared system

In order to improve tracking ability, an adaptive fusion algorithm based on adaptive neuro-fuzzy inference system (ANFIS) for radar/infrared system is proposed, which combines the merits of fuzzy logic and neural network. Fuzzy adaptive fusion algorithm is a powerful tool to make the actual value of the residual covariance consistent with its theoretical value. To overcome the defect of the dependence on the knowledge of the process and measurement noise statistics of Kalman filter, neural network is introduced, which has the ability to learn from examples and extract the statistical properties of the examples during the training sessions. The fusion system mainly consists of Kalman filters, ANFIS sensor confidence estimators (ASCEs) based on contextual information (CI) theory, knowledge base (KB) and track-to-track fusion algorithms. Experimental data are implemented to train ASCEs to obtain sensor confidence degree. Simulation results show that the algorithm can effectively adjust the system to adapt contextual changes and has strong fusion capability in resisting uncertain information.     

一种模糊QR滤波及在组合导航中的仿真研究

总结了常用的自适应滤波的方法，并提出了一种基于模糊逻辑的自适应卡尔曼滤波技术，用模糊逻辑自适应推理器来“在线”修正卡尔曼滤波系统噪声协方差Q和测量噪声协方差R，从而使滤波器不断执行最优估计。仿真结果表明该方法可以提高GPS／INS组合导航系统的精度和可靠性。     

基于无迹卡尔曼滤波传感器信息融合的车辆导航算法

针对复杂道路条件下车辆的导航问题,将全球定位系统(GPS)与车载终端传感器系统相结合,提出了基于多传感器系统的车辆精确定位模型,并针对扩展类卡尔曼滤波易产生突发性误差而导致的安全问题,采用基于Sigma点的无迹卡尔曼滤波器(UKF)传感器信息融合算法。根据实时的道路状况和车辆自身的运动状态给出符合要求的状态估值,实验与基于多项式扩展卡尔曼滤波车辆传感器信息融合算法在精度和效率方面进行了比较,结果表明,基于UKF传感器信息融合的算法在复杂路况下的估计精度和运行效率都有显著提高,能够根据当前的路线情况和车载传感器的反馈信息快速地估计出车辆的运动状态,实时计算出动态的车辆控制输入。     

基于UKF的UWB和GPS融合定位算法

智能汽车的发展对高精度定位需求日益显现. 针对汽车在城市建筑群、立交桥等特定环境下, 可见GPS卫星数量下降、车载GPS和惯性测量单元(inertial measurement unit, IMU)组合定位系统中IMU产生积累误差导致不能精确定位问题, 本文提出一种基于无迹卡尔曼滤波(unscented Kalman ...     

基于多传感器信息融合的移动机器人导航系统

介绍了多传感器信息融合的基本原理,给出了基于多传感器信息融合的移动机器人导航系统结构。建立了移动机器人数学模型,运用基于扩展卡尔曼滤波的信息融合方法实现了移动机器人导航算法。通过实验验证了基于多传感器信息融合的移动机器人导航系统和导航算法的有效性。     

带观测滞后和不确定噪声方差的多智能体传感网络鲁棒序贯协方差交叉融合Kalman滤波（英文）

This paper deals with the problem of designing robust sequential covariance intersection(SCI) fusion Kalman filter for the clustering multi-agent sensor network system with measurement delays and uncertain noise variances. The sensor network is partitioned into clusters by the nearest neighbor rule. Using the minimax robust estimation principle, based on the worst-case conservative sensor network system with conservative upper bounds of noise variances, and applying the unbiased linear minimum variance(ULMV) optimal estimation rule, we present the two-layer SCI fusion robust steady-state Kalman filter which can reduce communication and computation burdens and save energy sources, and guarantee that the actual filtering error variances have a less-conservative upper-bound. A Lyapunov equation method for robustness analysis is proposed, by which the robustness of the local and fused Kalman filters is proved. The concept of the robust accuracy is presented and the robust accuracy relations of the local and fused robust Kalman filters are proved. It is proved that the robust accuracy of the global SCI fuser is higher than those of the local SCI fusers and the robust accuracies of all SCI fusers are higher than that of each local robust Kalman filter. A simulation example for a tracking system verifies the robustness and robust accuracy relations.     

基于混合滤波最大期望算法的高速列车建模

针对高速列车非线性单质点模型的特殊结构及含有隐含变量问题, 提出一种基于混合滤波的最大期望辨识方法. 借助递阶辨识理论, 将高铁列车状态空间模型分解为线性子系统模型和非线性子系统模型. 进而, 分别利用卡尔曼滤波和粒子滤波对速度和位移状态进行联合估计. 最后, 使用最大期望方法辨识高铁列车子系统模型参数, 解决了隐含变量辨识问题. 和传统方法相比, 本文所提出方法计算量小, 且具有较高的辨识精度. 仿真对比实验结果验证了该方法的有效性.     

带观测滞后和不确定噪声方差的多智能体传感网络鲁棒序贯协方差交叉融合Kalman滤波

针对带观测滞后和不确定噪声方差的分簇多智能体传感网络系统,研究鲁棒序贯协方差交叉融合Kalman滤波器的设计问题.应用最邻近法则,传感网络被分成簇.应用极大极小鲁棒估计原理,基于带噪声方差最差保守上界的最差保守传感网络系统,提出了两级序贯协方差交叉(SCI)融合鲁棒稳态Kalman滤波器,可减小通信和计算负担并节省能量,且保证实际滤波误差方差有一个最小保守上界.一种Lyapunov方程方法被提出用于证明局部和融合滤波器的鲁棒性.提出了鲁棒精度的概念且证明了局部和融合鲁棒Kalman滤波器的鲁棒精度关系.证明全局SCI融合器的鲁棒精度高于每簇SCI融合器的精度且两者的鲁棒精度都高于每个局部鲁棒滤波器的精度.一个跟踪系统的仿真例子证明了鲁棒性和鲁棒精度关系.     

Correlation-Based Scan Matching Using Ultrasonic Sensors for EKF Localization

This paper presents a localization method for a mobile robot equipped with only low-cost ultrasonic sensors. Correlation-based Hough scan matching was used to obtain the robot’s pose without any predefined geometric features. A local grid map and a sound pressure model of ultrasonic sensors were used to acquire reliable scan results from uncertain and noisy ultrasonic sensor data. The robot’s pose was measured using correlation-based Hough scan matching, and the covariance was calculated. Localization was achieved by fusing the measurements from scan matching with the robot’s motion model through the extended Kalman filter. Experimental results verified the performance of the proposed localization method in a real home environment.     

GPS动态定位中卡尔曼滤波模型的建立及其强跟踪算法研究

提出一种改进的强跟踪卡尔曼滤波算法,应用于GPS动态定位滤波中获得明显效果。首先建立了一种新的GPS动态定位滤波模型,该模型与以往采用的非线性卡尔曼滤波模型相比,具有模型简单、实时性好的特点。为了进一步提高滤波器的动态性能,改进了文献[1]中的强跟踪滤波算法,大大提高了滤波器的跟踪能力。     

基于数据驱动的CRH高速列车悬挂系统早期故障检测

作为CRH(China railway high-speed)高速列车的重要组成部分,悬挂系统的可靠性对列车的安全运行和乘坐舒适性具有重要意义,为此,利用悬挂系统传感器数据,提出一种基于数据驱动的早期故障检测方法.首先,根据系统动态搭建列车悬挂系统Simpack模型,其中作动器的主动控制力作为系统输入,轨道不平顺由不平...     

基于伪距的GPS/INS滤波算法设计及仿真

提出一种以伪距和伪距率作为观测量的GPS/INS组合导航算法,使其适用于嵌入式GPS/INS组合导航系统产品的开发.针对目前市面卜的AHRS(姿态航向参考系统)的输出数据特性,以及课题组开发的GPS接收机的输出数据特性,提出一种简单易行的卡尔曼滤波算法.将姿态四元数递推方程与ECEF系下的位置、速度递推方程分别进行卡尔曼滤波,然后将滤波后得到的结果按照WGS-84模型转换为地理位置和速度.Matlab仿真与嵌入式系统运行结果表明设计的组合系统结构简单,稳定性好.避免了传统GPS/INS组合导航算法,要求惯性传感器精度高,计算量大,耗费硬件资源多的问题.