Data acquisition,analysis and applications of multi-sensor integration

This paper presents some key techniques for multi-sensor integration system, which is applied to the intelligent transportation system industry and surveying and mapping industry, e.g. road surface condition detection, digital map making. The techniques are synchronization control of multi-sensor, space-time benchmark for sensor data, and multi-sensor data fusion and mining. Firstly, synchronization control of multi-sensor is achieved through a synchronization control system which is composed of a time synchronization controller and some synchronization sub-controllers. The time synchronization controller can receive GPS time information from GPS satellites, relative distance information from distance measuring instrument and send space-time information to the synchronization sub-controller. The latter can work at three types of synchronization mode, i.e. active synchronization, passive synchronization and time service synchronization. Secondly, space-time benchmark can be established based on GPS time and global reference coordinate system, and can be obtained through position and azimuth determining system and synchronization control system. Thirdly, there are many types of data fusion and mining, e.g. GPS/Gyro/DMI data fusion, data fusion between stereophotogrammetry and PADS, data fusion between laser scanner and PADS, and data fusion between CCD camera and laser scanner. Finally, all these solutions presented in paper have been applied to two areas, i.e. land-borne intelligent road detection and measurement system and 3D measurement system based on unmanned helicopter. The former has equipped some highway engineering Co., Ltd. and has been successfully put into use. The latter is an ongoing research.

Weighted Multi-sensor Data Level Fusion Method of Vibration Signal Based on Correlation Function

As the differences of sensor＇s precision and some random factors are difficult to control,the actual measurement signals are far from the target signals that affect the reliability and precision of rotating machinery fault diagnosis.The traditional signal processing methods,such as classical inference and weighted averaging algorithm usually lack dynamic adaptability that is easy for trends to cause the faults to be misjudged or left out.To enhance the measuring veracity and precision of vibration signal in rotary machine multi-sensor vibration signal fault diagnosis,a novel data level fusion approach is presented on the basis of correlation function analysis to fast determine the weighted value of multi-sensor vibration signals.The approach doesn＇t require knowing the prior information about sensors,and the weighted value of sensors can be confirmed depending on the correlation measure of real-time data tested in the data level fusion process.It gives greater weighted value to the greater correlation measure of sensor signals,and vice versa.The approach can effectively suppress large errors and even can still fuse data in the case of sensor failures because it takes full advantage of sensor＇s own-information to determine the weighted value.Moreover,it has good performance of anti-jamming due to the correlation measures between noise and effective signals are usually small.Through the simulation of typical signal collected from multi-sensors,the comparative analysis of dynamic adaptability and fault tolerance between the proposed approach and traditional weighted averaging approach is taken.Finally,the rotor dynamics and integrated fault simulator is taken as an example to verify the feasibility and advantages of the proposed approach,it is shown that the multi-sensor data level fusion based on correlation function weighted approach is better than the traditional weighted average approach with respect to fusion precision and dynamic adaptability.Meantime,the approach is adaptable and easy to use,can be applied to other areas of vibration measurement.

STUDY ON THE COAL-ROCK INTERFACE RECOGNITION METHOD BASED ON MULTI-SENSOR DATA FUSION TECHNIQUE

The coal-rock interface recognition method based on multi-sensor data fusiontechnique is put forward because of the localization of single type sensor recognition method. Themeasuring theory based on multi-sensor data fusion technique is analyzed, and hereby the testplatform of recognition system is manufactured. The advantage of data fusion with the fuzzy neuralnetwork (FNN) technique has been probed. The two-level FNN is constructed and data fusion is carriedout. The experiments show that in various conditions the method can always acquire a much higherrecognition rate than normal ones.

A Novel Multi-sensor Data Fusion Algorithm and Its Application to Diagnostics

To Meet the requirements of multi-sensor data fusion in diagnosis for complex equipment systems,a novel, fuzzy similarity-based data fusion algorithm is given. Based on fuzzy set theory, it calculates the fuzzy similarity among a certain sensor's measurement values and the multiple sensor's objective prediction values to determine the importance weigh of each sensor,and realizes the multi-sensor diagnosis parameter data fusion.According to the principle, its application software is also designed. The applied example proves that the algorithm can give priority to the high-stability and high -reliability sensors and it is laconic ,feasible and efficient to real-time circumstance measure and data processing in engine diagnosis.

Multi-sensor measurement and data fusion technology for manufacturing process monitoring:a literature review

Due to the rapid development of precision manufacturing technology,much research has been conducted in the field of multisensor measurement and data fusion technology with a goal of enhancing monitoring capabilities in terms of measurement accuracy and information richness,thereby improving the efficiency and precision of manufacturing.In a multisensor system,each sensor independently measures certain parameters.Then,the system uses a relevant signalprocessing algorithm to combine all of the independent measurements into a comprehensive set of measurement results.The purpose of this paper is to describe multisensor measurement and data fusion technology and its applications in precision monitoring systems.The architecture of multisensor measurement systems is reviewed,and some implementations in manufacturing systems are presented.In addition to the multisensor measurement system,related data fusion methods and algorithms are summarized.Further perspectives on multisensor monitoring and data fusion technology are included at the end of this paper.

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.

A Study of Multi-sensor Data Fusion System Based on MAS for Nutrient Solution Measurement

For complementarity and redundancy of multi-sensor data fusion (MSDF) system,it is an effective approach for multiple components measurement.In order to measure nutrient solution on-line,a dynamic and complex system under greenhouse environment,sensors should have intelligent properties including self-calibration and self-compensation. Meanwhile,it is necessary for multiple sensors to cooperate and interact for enhancing reliability of multi-sensor system. Because of the properties of multi-agent system (MAS),it is an appropriate tool to study MSDF system.This paper proposed an architecture of MSDF system based on MAS for the multiple components measurement of nutrient solution.The sensor agent's structure and function modules are analyzed and described in detail,the formal definitions are given,too.The relations of the sensors are modeled to implement reliability diagnosis of the multi-sensor system,so that the reliability of nutrient control system is enhanced.This study offers an effective approach for the study of MSDF.

Unequal-interval data fusion algorithm for inertial/gravity matching integrated navigation system

Inertial/gravity matching integrated navigation system can effectively improve the longendurance navigation ability of underwater vehicles.Through the analysis of the matching process,the problem of unequal-interval in matching trajectory is addressed by an unequal-interval data fusion algorithm which is based on the unequal-interval characteristics analysis of the matching trajectory.Compared with previously available methods,the proposed algorithm improves the location precision.In conclusion,simulations of the integrated navigation system demonstrated the effectiveness and superiority of the proposed algorithm.

Multi-sensor Data Fusion by Improved Hough Transformation

In this paper we present an evidence-gathering approach to slove the multi-sensor data fusion problem. It uses an improved Hough transformation method rather than the usual statistical or geometric approach to extract the directions and positions of the walls in a room and update the location (orientation and position)of a mobile robot. The simulation results show that the proposed method is of practical importance since it is very simple and easy to implement.

Fuzzy Self-adaptive Proportional Integration Differential Control for Attitude Stabilization of Quadrotor UAV

The technology of attitude control for quadrotor unmanned aerial vehicles(UAVs) is one of the most important UAVs' research areas.In order to achieve a satisfactory operation in quadrotor UAVs having proportional integration differential(PID) controllers,it is necessary to appropriately adjust the controller coefficients which are dependent on dynamic parameters of the quadrotor UAV and any changes in parameters and conditions could affect desired performance of the controller.In this paper,combining with PID control and fuzzy logic control,a kind of fuzzy self-adaptive PID control algorithm for attitude stabilization of the quadrotor UAV was put forward.Firstly,the nonlinear model of six degrees of freedom(6-DOF) for quadrotor UAV is established.Secondly,for obtaining the attitude of quadrotor,attitude data fusion using complementary filtering is applied to improving the measurement accuracy and dynamic performance.Finally,the attitude stabilization control simulation model of the quadrotor UAV is build,and the self-adaptive fuzzy parameter tuning rules for PID attitude controller are given,so as to realize the online self-tuning of the controller parameters.Simulation results show that comparing with the conventional PID controller,this attitude control algorithm of fuzzy self-adaptive PID has a better dynamic response performance.

Weight Data Fusion Based on Mutual Support Applied in Large Diameter Measurement

The on-line diameter measurement of larger axis workpieces is hard to achieve high precision detection, because of the bad environment of locale, the problem to amend the measuring error by non-uniform temperature field, and the difficulty to collimate and locate by usual method. By improving the measurement accuracy of larger axis accessories, it is useful to raise axis and hole＇s industry produce level. Because of the influence of complex environment in locale and some influential factors which are hard excluded from the large diameter measurement with multi-rolling-wheels method, the measurement results may not support or even contradict each other. To the situation, this paper puts forward a mutual support deviation distinguish data fusion method, including mutual support deviation detection and weight data fusion. The mutual support deviation detection part can effectively remove or weaken the unexpected impact on the measurement results and the weight data fusion part can get more accurate estimate result to the detected data. So the method can further improve the reliability of measurement results and increase the accuracy of the measurement system. By using the weight data fusion based on the mutual support （DFMS） to the simulation and experiment data, both simulation results and experiment results show that the method can effectively distinguish the data influenced by unexpected impact and improve the stability and reliability of measurement results. The new provided mutual support deviation distinguish method can be used to single sensor measurement and multi-sensor measurement, and can be used as a reference in the data distinguish of other area. The DFMS is helpful to realize the diameter measurement expanded uncertainty in 5 ×10^-6D or even higher when the measured axis workpiece＇s diameter is 1-5 m （ 1 m ≤ D ≤5 m ）.

Research on Intelligent Fusion Method of Multisensor Data in Internet of Things

A huge network formed by the combination of information gathering and the Internet aims to realize the connection between things and things,things and people,all goods and networks,and facilitate the identification,management and control of the Internet of Things.In order to solve the inconsistency of various types of sensor communication protocols in the Internet of Things and the problem of data analysis,Association and evaluation of real-time multi-sensor data acquisition for different business applications,it is of great significance to develop multi-sensor protocol integration and information fusion.The method of simulating the sensor data simulates the converted data,and then according to the specific requirements of the measurement,the same type of data obtained by various sensors is converted into different physical quantities they represent.According to the theoretical analysis and ontology relationship of sensor data fusion in the Internet of Things,as well as the general method of sensor ontology intelligent fusion,the application in multi-sensor data fusion in the Internet of Things lays a foundation for intelligent control and decision analysis of the Internet of Things system.

Integrated Navigation Based on Robust Estimation Outputs of Multi-sensor Measurements and Adaptive Weights of Dynamic Model Information

An integrated navigation based on the kinematic or dynamic state model and the raw measurements has the advantages of high redundancy, high reliability, as well as high ability of fault tolerance and simplicity in calculation. In order to control the influences of measurements outliers and the kinematic model errors on the integrated navigation results, a robust estimation method and an adaptive data fusion method are applied. An integrated navigation example using simulated data is performed and analyzed.

Application of data fusion on multi-function earth drill

taking the bucket of multi function earth drill as an example, combining with the conception of multi sensor integration and data fusion, adopting the terrene column chart and digging torque formula as control dependence, the detecting method of the earth drill’s working state is introduced. Multi sensor data fusion is done with the aid of BP neural network in Matlab. The data to be interfused are pre processed and the program of simulation and “point checking” is given.

基于地名地址的多源异构数据融合方法研究

为解决不同行业政务信息资源的融合,实现智慧城市各行业数据的共享,深入挖掘和发挥数据价值,提出一种基于地名地址匹配的数据融合方法,分别对含空间坐标和仅包含位置描述的两种不同类型政务数据进行匹配,提出利用分区单元计算、构建分级分词地名地址数据库和文本模糊匹配等关键技术,提高了地名地址匹配的效率和准确度。并通过在智慧蓉城建设项目中实践应用,证实该方法能有效提升匹配效率和精度,实现多源、异构、海量政务数据的高效融合。

多站协同感知技术

6G时代,全频谱通信将成为一个特征,雷达频段与通信频段共存共用可能成为现实。通感一体化技术可以复用通信设备的硬件和频谱,对物理世界进行无线感知,已被ITU列为6G的六大场景技术之一。利用现有蜂窝网络中广泛部署基站的数量优势,多站协同感知是6G通感一体化的核心关键,可以为感知性能带来显著增益。针对通信网络的多站协同感知研究仍然处于初步阶段,缺乏协同场景定义和关键技术识别,文章提供多站协同感知的应用场景,并给出实现多站协同感知的关键技术,为多站协同感知的深入研究提供技术参考。

省级自然资源监测数据库建设研究

针对自然资源监测数据成果集成管理与动态更新需求,开展了省级自然资源监测数据库建设研究。在数据分层、数据模型、逻辑设计、物理实现等设计基础上,详细阐述了数据库建库与更新的技术流程。通过空间数据多时态管理技术、多源矢量数据关联融合、基于身份标识码的全生命周期管理、按需更新的增量更新模式等关键技术应用实施,解决数据标准化整合与数据库动态更新问题,从而为自然资源监测数据库纳入自然资源三维立体时空数据库体系提供支持。

基于数据中台的数据融合集成关键技术研究

当前开发新的业务系统往往需要从其他系统引接数据,系统之间的直接数据交互增加了系统开发的复杂性。为解决上述问题,研究基于数据中台的数据融合集成关键技术,为数据中台建设提供参考。设计数据中台技术架构,实现数据引接、数据管理和数据服务。覆盖数据生命周期,设计数据中台业务流程。瞄准数据赋能目标,提升数据融合集成核心能力,采用混合架构完成多源异构数据存储,利用数据层次模型实现数据映射,应用知识图谱进行数据关联。提出的数据中台技术架构已在实际项目中运用,达到预期效果。