Data Fusion in Distributed Multi-sensor System

This paper presents a data fusion method in distributed multi-sensor system including GPS and INS sensors’ data processing. First, a residual χ 2 \|test strategy with the corresponding algorithm is designed. Then a coefficient matrices calculation method of the information sharing principle is derived. Finally, the federated Kalman filter is used to combine these independent, parallel, real\|time data. A pseudolite (PL) simulation example is given.

Disparity estimation for multi-scale multi-sensor fusion

The perception module of advanced driver assistance systems plays a vital role.Perception schemes often use a single sensor for data processing and environmental perception or adopt the information processing results of various sensors for the fusion of the detection layer.This paper proposes a multi-scale and multi-sensor data fusion strategy in the front end of perception and accomplishes a multi-sensor function disparity map generation scheme.A binocular stereo vision sensor composed of two cameras and a light deterction and ranging(LiDAR)sensor is used to jointly perceive the environment,and a multi-scale fusion scheme is employed to improve the accuracy of the disparity map.This solution not only has the advantages of dense perception of binocular stereo vision sensors but also considers the perception accuracy of LiDAR sensors.Experiments demonstrate that the multi-scale multi-sensor scheme proposed in this paper significantly improves disparity map estimation.

Research on Transmission Line Tower Tilting and Foundation State Monitoring Technology Based onMulti-Sensor Cooperative Detection and Correction

The transmission line tower will be affected by bad weather and artificial subsidence caused by the foundation and other factors in the power transmission.The tower’s tilt and severe deformation will cause the building to collapse.Many small changes caused the tower’s collapse,but the early staff often could not intuitively notice the changes in the tower’s state.In the current tower online monitoring system,terminal equipment often needs to replace batteries frequently due to premature exhaustion of power.According to the need for real-time measurement of power line tower,this research designed a real-time monitoring device monitoring the transmission tower attitude tilting and foundation state based on the inertial sensor,the acceleration of 3 axis inertial sensor and angular velocity raw data to pole average filtering pre-processing,and then through the complementary filtering algorithm for comprehensive calculation of tilt angle,the system meets the demand for inclined online monitoring of power line poles and towers regarding measurement accuracy,with low cost and power consumption.The optimization multi-sensor cooperative detection and correction measured tilt angle result relative accuracy can reach 1.03%,which has specific promotion and application value since the system has the advantages of unattended and efficient calculation.

A multi-sensor-based distributed real-time measurement system for glacier deformation

Glacier disasters occur frequently in alpine regions around the world,but the current conventional geological disaster measurement technology cannot be directly used for glacier disaster measurement.Hence,in this study,a distributed multi-sensor measurement system for glacier deformation was established by integrating piezoelectric sensing,coded sensing,attitude sensing technology and wireless communication technology.The traditional Modbus protocol was optimized to solve the problem of data identification confusion of different acquisition nodes.Through indoor wireless transmission,adaptive performance analysis,error measurement experiment and landslide simulation experiment,the performance of the measurement system was analyzed and evaluated.Using unmanned aerial vehicle technology,the reliability and effectiveness of the measurement system were verified on the site of Galongla glacier in southeastern Tibet,China.The results show that the mean absolute percentage errors were only 1.13%and 2.09%for the displacement and temperature,respectively.The distributed glacier deformation real-time measurement system provides a new means for the assessment of the development process of glacier disasters and disaster prevention and mitigation.

Mission scheduling of multi-sensor collaborative observation for space surveillance network

With increased dependence on space assets,scheduling and tasking of the space surveillance network(SSN)are vitally important.The multi-sensor collaborative observation scheduling(MCOS)problem is a multi-constraint and high-conflict complex combinatorial optimization problem that is nondeterministic polynomial(NP)-hard.This research establishes a sub-time window constraint satisfaction problem(STWCSP)model with the objective of maximizing observation profit.Considering the significant effect of genetic algorithms(GA)on solving the problem of resource allocation,an evolution heuristic(EH)algorithm containing three strategies that focus on the MCOS problem is proposed.For each case,a task scheduling sequence is first obtained via an improved GA with penalty(GAPE)algorithm,and then a mission planning algorithm(heuristic rule)is used to determine the specific observation time.Compared to the model without sub-time windows and some other algorithms,a series of experiments illustrate the STWCSP model has better performance in terms of total profit.Experiments about strategy and parameter sensitivity validate its excellent performance in terms of EH algorithms.

Progress and Achievements of Multi-sensor Fusion Navigation in China during 2019—2023

Global Navigation Satellite System(GNSS)can provide all-weather,all-time,high-precision positioning,navigation and timing services,which plays an important role in national security,national economy,public life and other aspects.However,in environments with limited satellite signals such as urban canyons,tunnels,and indoor spaces,it is difficult to provide accurate and reliable positioning services only by satellite navigation.Multi-source sensor integrated navigation can effectively overcome the limitations of single-sensor navigation through the fusion of different types of sensor data such as Inertial Measurement Unit(IMU),vision sensor,and LiDAR,and provide more accurate,stable and robust navigation information in complex environments.We summarizes the research status of multi-source sensor integrated navigation technology,and focuses on the representative innovations and applications of integrated navigation and positioning technology by major domestic scientific research institutions in China during 2019—2023.

异步测量系统的分布式序贯椭球相交融合估计

针对具有测量缺失现象的多源异步测量系统的状态估计,提出一种基于条件Kalman滤波的分布式序贯椭球相交(DSEI)融合算法。对系统测量缺失现象通过一组服从伯努利分布的随机变量进行建模描述;同步化状态空间模块利用状态迭代方法将异步测量系统在测量更新时刻进行同步;局部滤波模块采用条件Kalman滤波算法实现,保证滤波结果的完整性;融合估计模块在不考虑多传感器间相关性信息的前提下,设计DSEI融合算法对各个传感器提供的滤波结果进行融合。仿真实例验证了所提算法具有精度提高35%、迭代时间小于3 ms的优越性能。

基于多源传感技术的铁路货运智慧监控模拟实验系统设计

在“新工科”教育背景下,为进一步丰富和优化铁路货运实验教学内容,设计了基于多源传感技术的铁路货物运输智慧监控模拟实验系统,即使用多源传感信息技术对货物运输环境和货物运输状态进行监测以及预警。结合OBE教育理念开展教学改革实践,提出提高学生综合能力的教学和评价方法。学生能对铁路货物运输的全过程有更为深刻的认识,提升对货物运输系统的操作能力,为今后从事铁路货运工作打下坚实的基础。

带DoS攻击补偿的线性离散系统分布式融合滤波器

多传感器网络化线性离散系统的每个传感器基于自己的观测数据可进行局部状态估计。当局部估值被传输给融合中心时,可能遭受DoS(Denial of service)攻击。为了补偿DoS攻击引起的数据丢失,采用丢失数据的预报器进行补偿。应用线性无偏最小方差矩阵加权融合算法获得分布式融合状态滤波器。所提出的分布式融合滤波器改善了局部估计的精度,且比协方差交叉融合算法具有更高的估计精度。仿真例子验证了算法的有效性。

核电厂边坡自动化监测系统的设计与实现

边坡工程具有地形险峻、地质结构复杂和事故突发等特点,影响着电厂的安全运营,对其进行变形监测是保证边坡安全运行的重要措施。随着变形监测技术越来越向着自动性、连续性、实时性以及远程控制性方向发展,将全球导航卫星系统(Global Navigation Satellite System,简称GNSS)、激光测缝仪和测斜仪等多元传感器技术与4G通讯技术、互联网技术相结合构建自动化监测系统,并将其应用到核电厂边坡工程的变形监测中,通过监测数据自动化接收、数据处理与分析、报表生成和预警预报等模块设计,实现了对边坡的位移、地表裂缝和深层位移等24 h远程监测,获得边坡局部和整体变形及变形趋势,做好预警预报工作,为边坡隐患核查和消除提供技术支撑。结果显示:监测数据均在预警值之内,边坡处于稳定变形状态,同时为验证GNSS位移监测系统数据可靠性,开展人工监测,并将观测成果与GNSS监测数据进行对比分析,认为该系统数据可靠性较高,满足核电厂边坡工程监测的需要。结合工程实践验证了自动化监测系统在核电厂边坡安全管理工作中的可行性,为后续相关项目的实施提供一定的借鉴意义。

基于IQPSO-EKF的多传感器融合姿态测量方法研究

为解决自动化竖井掘进设备的定位调姿精度对竖井、孔桩挖掘效率与质量的影响,提出了一种基于改进量子粒子群(IQPSO)-扩展卡尔曼滤波(EKF)的姿态测量算法,以提高微机电系统(MEMS)传感器测量精度。首先,对MEMS传感器数据进行了预处理(除噪、滤波、校准等);然后,参考现有飞行器的坐标系,建立了姿态解算模型,通过姿态角数学模型及运动学分析,构建了EFK状态方程,针对EKF方法参数估计不准确的问题,以分段混沌映射优化初始种群,引入平均位置最优值来避免陷入局部最优的IQPSO-EFK算法,优化EKF的系统、测量噪声的协方差参数;最后,对改进算法和三组姿态误差估计进行了对比实验。研究结果表明:对比三种典型目标函数,IQPSO-EFK相较于普通粒子群算法(QPSO-EFK)具有更强的寻优能力与收敛精度;对比三组旋转速度姿态测量误差,基于IQPSO-EKF算法的姿态测量方法在测量误差时比真实测量误差减少了约86.3%,比扩展卡尔曼滤波减少了约68.7%,比普通粒子群算法减少了约28.2%,证明该算法有效地提高了MEMS传感器测量精度。

固定和切换拓扑下多智能体系统二分容错状态一致性研究

针对具有传感器故障的连续时间线性领导-跟随多智能体系统,研究了固定和切换拓扑下的二分容错状态一致性问题。首先,考虑不存在传感器故障的情况,提出了一种基于状态反馈的分布式二分一致性控制协议;其次,考虑存在传感器故障和状态不可测的情况,根据邻居交互信息构造新型分布式状态和故障观测器,并提出了基于输出反馈的分布式二分容错一致性控制协议;最后,进一步考虑通信拓扑发生切换的情况,提出了一种基于观测器的输出反馈二分容错一致性控制协议。理论分析和仿真算例均验证了不同情况下提出的分布式控制策略能够消除传感器故障带来的不良影响,同时,可实现预期的二分状态一致。

Research on Optimal Preload Method of Controllable Rolling Bearing Based on Multisensor Fusion

Angular contact ball bearings have been widely used in machine tool spindles,and the bearing preload plays an important role in the performance of the spindle.In order to solve the problems of the traditional optimal preload prediction method limited by actual conditions and uncertainties,a roller bearing preload test method based on the improved D-S evidence theorymulti-sensor fusion method was proposed.First,a novel controllable preload system is proposed and evaluated.Subsequently,multiple sensors are employed to collect data on the bearing parameters during preload application.Finally,a multisensor fusion algorithm is used to make predictions,and a neural network is used to optimize the fitting of the preload data.The limitations of conventional preload testing methods are identified,and the integration of complementary information frommultiple sensors is used to achieve accurate predictions,offering valuable insights into the optimal preload force.Experimental results demonstrate that the multi-sensor fusion approach outperforms traditional methods in accurately measuring the optimal preload for rolling bearings.

基于多传感器融合的系统自我定位与地图重建

在图优化框架的基础上,设计多传感器融合方案和有效的优化方法,提出一套具有鲁棒性的定位与建图(Simultaneous Localization and Mapping,SLAM)方案,能够有效应对室内外复杂环境。进一步发展激光-视觉后端建图融合方法,构建具备全新地图表达形式的点云网格化地图。同时使用低成本传感器,设计实现基于多传感器融合的高性能低成本背包扫描系统,整体完成在未知环境中的自我定位和稠密建图,且在低性能CPU设备上将长时间运动带来的每100 m的轨迹误差平均降低至厘米级。提出的基于多传感器融合方案,在精度、算力消耗上能够匹配现有主流方案,对获取各种环境条件下的系统准确定位结果和丰富的空间信息具有重要意义。

基于图优化的GNSS/双目视觉/惯性SLAM系统开发及应用

为提高机器人室外长航时定位精度,提出一种基于图优化的全球导航卫星系统(GNSS)/双目视觉/惯性同时定位与建图(SLAM)系统开发及应用。将空间中的线特征作为几何约束的补充,集成至前端的特征提取及后端的位姿优化线程,提升位姿解算精度。同时,以因子图构建联合优化的图结构,并推导出全局观测误差模型。近200 m的BullDog-CX机器人巡检结果表明,所提算法相比于VINSFusion和PL-VINS分别取得约12.6%及3.4%的定位精度提升,为室外机器人长航时导航提供了一种可行方案。

基于单传感器的三波段共口径光学系统设计

针对现有多波段成像系统体积大、功耗高和集成化设计困难的问题,本文提出了一种基于单传感器的三波段共口径成像光学系统的设计方法。首先,在光学系统的光阑处设计1×2多波段透镜阵列,把可见光波段和短波红外波段同时成像在一个像平面上,并把两个波段中心波长的成像位置偏差控制在一个像元内以实现双波段融合成像。然后,针对双波段成像衍射极限不同的问题,提出分通道透镜阵列的离轴偏移量和通光口径大小联合优化方法,并采用双电动光阑高速控制三个成像通道的切换速度。最后,设计了一个基于单传感器的焦距为30 mm,工作波段分别为480~900 nm、900~1700 nm和480~1700 nm的三波段共口径光学系统。设计及分析结果表明该系统具有成像质量好、结构紧凑、无运动光学元件、成像波段切换速度快等优点。

多传感器融合下船舶机电系统多发故障信号监测

为了提高船舶维护效率,提出一种多传感器融合下船舶机电系统多发故障信号监测方法。根据故障状态下的信号频率,使用小波变换法提取故障信号特征参数作为蚁群算法优化BP神经网络输入,实现多发故障诊断,并通过DS证据理论完成多传感器数据融合,得出故障诊断结果。实验结果表明,该方法可通过多传感器融合判断出船舶机电系统故障类型,即使一种传感器出现故障也不影响诊断效果,诊断船舶机电系统多发故障平均准确率高达97.02%,能够实现较为精准的船舶机电系统多发故障监测。

GNSS与多传感器集成的尾矿库监测系统设计

尾矿库发生灾害造成人员伤亡和财产损失较大,如何对尾矿库施行监测已经成为世界范围内讨论的问题。使用搭载i.MX6ULL处理器的Linux系统进行嵌入式开发,通过对嵌入式数据采集系统、云服务器后台程序和用户端远程监测程序三部分设计,完成了一种GNSS与多传感器集成的尾矿库监测系统。该系统具有GNSS高精度位移、雨量、倾斜、孔隙压力和水位的数据采集,云平台数据传输与存储,用户远程控制与实时监测的功能。通过进行实地模拟实验,结果表明该系统运行稳定、可靠性高、各传感器精度达到规范要求,满足对尾矿库远程监测的需求。

Optimal fusion state estimator for a multi-sensor system subject to multiple packet dropouts

In this note,we study the state estimation problem for a multi-sensor system subject to multiple packet dropouts.A novel optimal distributed fusion estimator is derived by applying a resending mechanism and a parallel information filtering structure.It is shown that the proposed distributed fusion estimator has smaller estimation error covariance and less computation complexity when compared with the centralised Kalman like estimator with multiple intermittent measurements.

基于多传感器的扫地机器人导航系统避障研究

探讨了扫地机器人导航系统中基于多传感器的避障问题。通过设计一套多传感器避障方案,结合多传感器数据融合算法、避障路径规划算法,并考虑其他潜在影响因素,实现扫地机器人导航系统性能。