Train distance and speed estimation using multi sensor data fusion

Muniandi, Ganesan; Ezhilarasi, D.

doi:10.1049/iet-rsn.2018.5359

Cited by 26 publications

(11 citation statements)

References 23 publications

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“…The 5G urban component sensor sensing node is the most basic element in the wireless sensor network system. It usually has the function of sensing and detecting certain spatial parameters, temporary storage and calculation of data, and wireless communication, and it is powered by the battery of each node [21]. Data fusion is based on the sampled data from multiple sensors, and the data is integrated and calculated to obtain the final result [22].…”

Section: Mathematical Model Of Data Fusionmentioning

confidence: 99%

Information Processing Methods and LOT Cloud Platform for Fusion of 5G Urban Component Sensors

2022

Mobile Information Systems

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With the rapid development of intelligent applications, the development of the Internet of Things changes with each passing day. The Internet of Things, as an essential component of the new information technology, has already become another revolutionary information industry after computers and modern communications. Although the Internet of Things has developed and grown, at the application level, the applications of Internet of Things devices of different architectures are relatively independent of their data information. This paper is based on the research of 5G urban component sensor information processing method and LOT cloud platform, and proposes a data fusion algorithm based on the data sequence of the same time. It analyzes the system process and architecture diagram of the LOT cloud platform, examines the performance of the Hadoop platform on different scale data sets, and uses the stand-alone system as a reference. The experimental results show that the time increase of the cluster decreases with the increase of the amount of data, which shows that the system exerts the advantages of parallel computing under the massive amount of data, and as the data source file increases, the accuracy rate decreases. When the data source size is 2000 MB, the Hadoop cluster system with 3 and 4 cluster nodes has the highest accuracy, 79.21% and 76.28%, respectively.

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Section: Mathematical Model Of Data Fusionmentioning

confidence: 99%

Information Processing Methods and LOT Cloud Platform for Fusion of 5G Urban Component Sensors

2022

Mobile Information Systems

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“…Multisensor fusion technology utilizes multiple sensor resources to integrate redundant multisensor information in time and space, while eliminating errors or unreliable information, so as to obtain a consistent description or explanation of the object described. As a result, the sensor system has better performance than a system composed of various subsets [16].…”

Section: Features Of Multisensor Information Fusionmentioning

confidence: 99%

Research on Software Design of Intelligent Sensor Robot System Based on Multidata Fusion

Liu

Wang

2021

Journal of Sensors

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With the advent of robots combined with artificial intelligence, robots have become an indispensable part of production and life. Especially in recent years, the collaboration between humans and machines has become a research trend in the field of robotics, with high work efficiency and flexibility. The advantages of safety and stability make intelligent robots the best choice for the current industrial and service industries with high labor intensity and hazardous working environments. This paper is aimed at studying the software design of an intelligent sensor robot system based on multidata fusion. In this paper, through the needle robot’s high precision requirements and the problem of fast response, a path design method based on the ant colony optimization (ACO) algorithm is proposed. Path planning is performed by intelligent robots for obstacle avoidance experiments, while global optimization is performed by the ant colony optimization (ACO) algorithm. For adaptive functions including obstacle reduction and path information length, the safest and shortest path is finally achieved through the ant colony optimization (ACO) algorithm. The experimental results show that using the ant colony optimization algorithm to perform simulation experiments and preprocessing operations on the data collected by the sensor can improve the accuracy and effectiveness of the data. The ant colony algorithm performs fusion and path planning, and on the basis of ensuring accuracy, it can speed up the convergence speed. Through the data analysis of obstacle avoidance experiments of intelligent robots, it can be concluded that it is very necessary for intelligent robots to install ultrasonic sensors and infrared sensors in obstacle avoidance, because the error between the test distance of the ultrasonic sensor and the infrared sensor and the actual distance is 0.001.

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“…As the high-speed railway network becomes denser and the number of trains running on the network increases, the safety of high-speed trains has become an issue of great concern [1,2]. There is a requirement for real-time, continuous, accurate, and reliable navigation information for the operation control system to control, schedule, and monitor train movements [1,3,4]. In tunnels, the GNSS signal is lost and there is a lack of other available information, which presents a challenge to these navigation systems [4].…”

Section: Introductionmentioning

confidence: 99%

High-Speed Train Tunnel Navigation Method Based on Integrated MIMU/ODO/MC Navigation

Sun

Tang

et al. 2021

Applied Sciences

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When a high-speed train is running in a tunnel, the global navigation satellite system (GNSS) signal is completely lost. Relying only on the inertial navigation system (INS) composed of Micro-electromechanical Systems (MEMS) devices leads to large navigation errors. To solve this problem, an integrated micro inertial measurement unit (MIMU), odometer (ODO), and motion constraint (MC) tunnel navigation method is proposed. This method first establishes a motion constraint model based on the installation angles of MIMU; secondly, the effect of turning on the motion constraint model and the odometer is analyzed and the use condition of the motion constraints is obtained; the installation angles of MIMU are then estimated when GNSS signal is good and the use condition of the motion constraints is met; finally, the forward speed measured by the odometer and the motion constraints are applied to suppress the error of the INS and improve the navigation accuracy in the tunnel. Based on this method, high-speed train navigation tests were carried out both in areal tunnel environment and in a case study with an artificially disconnected GNSS signal. The experimental results showed that the navigation accuracy of the train in the tunnel was significantly improved. Seamless navigation was achieved inside and outside the tunnel, which verified the effectiveness of the method.

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Train distance and speed estimation using multi sensor data fusion

Cited by 26 publications

References 23 publications

Information Processing Methods and LOT Cloud Platform for Fusion of 5G Urban Component Sensors

Information Processing Methods and LOT Cloud Platform for Fusion of 5G Urban Component Sensors

Research on Software Design of Intelligent Sensor Robot System Based on Multidata Fusion

High-Speed Train Tunnel Navigation Method Based on Integrated MIMU/ODO/MC Navigation

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