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.

Research on the Mechanism of Multi-Sensor Fusion Configuration Based on the Optimal Principle of the Vehicle

In order to address the issue of sensor configuration redundancy in intelligent driving,this paper constructs a multi-objective optimization model that considers cost,coverage ability,and perception performance.And then,combining a specific set of parameters,the NSGA-II algorithm is used to solve the multi-objective model established in this paper,and a Pareto front containing 24 typical configuration schemes is extracted after considering empirical constraints.Finally,using the decision preference method proposed in this paper that combines subjective and objective factors,decision scores are calculated and ranked for various configuration schemes from both cost and performance preferences.The research results indicate that the multi-objective optimization model established in this paper can screen and optimize various configuration schemes from the optimal principle of the vehicle,and the optimized configuration schemes can be quantitatively ranked to obtain the decision results for the vehicle under different preference tendencies.

State Estimation Method for GNSS/INS/Visual Multi-sensor Fusion Based on Factor Graph Optimization for Unmanned System

With the development of unmanned driving technology,intelligent robots and drones,high-precision localization,navigation and state estimation technologies have also made great progress.Traditional global navigation satellite system/inertial navigation system(GNSS/INS)integrated navigation systems can provide high-precision navigation information continuously.However,when this system is applied to indoor or GNSS-denied environments,such as outdoor substations with strong electromagnetic interference and complex dense spaces,it is often unable to obtain high-precision GNSS positioning data.The positioning and orientation errors will diverge and accumulate rapidly,which cannot meet the high-precision localization requirements in large-scale and long-distance navigation scenarios.This paper proposes a method of high-precision state estimation with fusion of GNSS/INS/Vision using a nonlinear optimizer factor graph optimization as the basis for multi-source optimization.Through the collected experimental data and simulation results,this system shows good performance in the indoor environment and the environment with partial GNSS signal loss.

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.

基于无线传感网络的现代建筑多传感安防预警系统设计

预警系统是现代建筑智慧体系中重要组成部分,对保证建筑安全具有重要作用,但目前系统功能与性能还有待提高,在实际中错误报警比例比较高,而且反应性能比较弱,提出基于无线传感网络的现代建筑多传感安防预警系统设计。系统采用应用层、业务逻辑层、网络层与感知层四层体系结构,硬件方面对温度、烟雾、湿度、图像多种无线传感器和报警器选型与设计,利用无线传感网络对传感数据传输,软件方面通过对多传感信息融合处理,评价建筑安全等级并预警响应,以此完成基于无线传感网络的现代建筑多传感安防预警系统设计。经实验证明,设计系统错误报警比例不超过1%,反应时间不超过0.25s,在现代建筑安全防护预警领域具有良好的应用前景。

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。

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。

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。

多传感约束下三维运动图像连带动作高质量仿真

因非刚体三维运动图像在成像过程中,传感器区域的随机性对动作限制大、操作复杂,导致动作虚拟精度较低、图像质量差。为此,提出了多传感约束下三维运动图像连带动作高质量仿真。根据多传感器受重力加速度以及地磁场的作用,求解传感器从原始状态到运动当前状态的角度变化。根据变化数值对图像进行去噪,提取连带动作特征。在Vicon系统中计算连带动作空间位置,捕捉连带动作变换轨迹,将运动轨迹结果输入PAR系统,完成连带动作虚拟成像。仿真结果表明:该方法图像去噪效果较好,传感器响应时间始终低于0.52 s,传感器响应速度较快,连带动作轨迹误差始终低于0.045,动作虚拟仿真精度较高,能够有效提高三维运动图像质量。

APPLICATION OF MULTI-SENSOR DATA FUSION BASED ON FUZZY NEURAL NETWORK IN ROTA TING MECHANICAL FAILURE DIAGNOSIS

At present, multi-se nsor fusion is widely used in object recognition and classification, since this technique can efficiently improve the accuracy and the ability of fault toleranc e. This paper describes a multi-sensor fusion system, which is model-based and used for rotating mechanical failure diagnosis. In the data fusion process, the fuzzy neural network is selected and used for the data fusion at report level. By comparing the experimental results of fault diagnoses based on fusion data wi th that on original separate data,it is shown that the former is more accurate than the latter.

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。

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。

基于卷积神经网络多传感器油液系统故障诊断

针对单个传感器无法提供掘进机油液系统在长期运行过程中数据互补性和多维性的问题,提出了基于卷积神经网络(CNN)的掘进机多传感器油液系统故障诊断方法。首先,油液监测方法是通过润滑油的理化性能指标和油液携带的磨粒信息反应油液的性能和状态,可以直接从油液数据中学习最好的特征,不需要任何形式的转换以及特征提取。其次,结合多个传感器采集到的数据作为CNN的输入来对油液系统进行故障诊断。最后,将提出的方法与其它机器学习方法在故障状态分类准确性方面进行比较,通过实验验证得到所提出方法的诊断精度高于其它方法,从而实现了高效诊断性能。

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。