Multi-sensor Data Fusion Technology Research Articles

Application Research and Improvement of Weighted Information Fusion Algorithm and Kalman Filtering Fusion Algorithm in Multi-sensor Data Fusion Technology

Application Research and Improvement of Weighted Information Fusion Algorithm and Kalman Filtering Fusion Algorithm in Multi-sensor Data Fusion Technology

A Fuzzy Dempster–Shafer Evidence Theory Method with Belief Divergence for Unmanned Surface Vehicle Multi-Sensor Data Fusion

The safe navigation of unmanned surface vehicles in the marine environment requires multi-sensor collaborative perception, and multi-sensor data fusion technology is a prerequisite for realizing the collaborative perception of different sensors. To address the problem of poor fusion accuracy for existing multi-sensor fusion methods without prior knowledge, a fuzzy evidence theory multi-sensor data fusion method with belief divergence is proposed in this paper. First of all, an adjustable distance for measuring discrepancies between measurements is devised to evaluate the degree of measurement closeness to the true value, which improves the adaptability of the method to different classes of sensor data. Furthermore, an adaptive multi-sensor measurement fusion strategy is designed for the case where the sensor accuracy is known in advance. Secondly, the affiliation function of the fuzzy theory is introduced into the evidence theory approach to assign initial evidence of measurements in terms of defining the degree of fuzzy support between measurements, which improves the fusion accuracy of the method. Finally, the belief Jensen–Shannon divergence and the Rényi divergence are combined for measuring the conflict between the evidence pieces to obtain the credibility degree as the reliability of the evidence, which solves the problem of high conflict between evidence pieces. Three examples of multi-sensor data fusion in different domains are employed to validate the adaptability of the proposed method to different kinds of multi-sensors. The maximum relative error of the proposed method for multiple sensor experiments is greater than or equal to 0.18%, and its error accuracy is much higher than the best result of 0.46% among other comparative methods. The experimental results verify that the proposed data fusion method is more accurate than other existing methods.

Research on fire source location and early warning algorithm based on sensor network

In the construction and development of modern society, the form of urban buildings presents the characteristics of diversification, although different types of building structure for social residents' life and production has brought convenient conditions, but also caused more safety risks, the building internal fire safety technology requirements are increasingly high. In this context, the traditional fire detection technology has been unable to meet the needs of building fire prevention, so scholars from various countries in the practice of exploration, integrated the use of network technology and intelligent algorithm to build an automatic fire alarm system, which can accurately detect and effectively control the fire in the early stage, can truly realize the prevention oriented development requirements. In this paper, based on the research status of building fire source location early warning technology in the new era, according to the basic principle of wireless sensor network system and fire location DV-hop algorithm, based on the multi-sensor data fusion technology to create a fire source early warning system. The final experimental results show that this algorithm can ensure the accuracy of fire warning positioning and improve the scientific and accurate decision making of the system.

An Intelligent Implementation of Multi-Sensing Data Fusion With Neuromorphic Computing for Human Activity Recognition

The increasing demand for considering multisensor data fusion technology has drawn attention for precise human activity recognition (HAR) over standalone technology due to its reliability and robustness. This article presents a framework that fuses data from multiple sensing systems and applies neuromorphic computing to sense and classify human activities. The data is collected by utilizing inertial measurement unit (IMU) sensors, software-defined radios, and radars, and feature extraction and selection are performed on the data. For each of the actions, such as sitting and standing, an activity matrix is generated, which is then fed into a discrete Hopfield neural network as a binary feature pattern for one-shot learning. Following the Hopfield network neurons’ feedback output, the conformity to the standard activity feature pattern is also determined. Following the Hopfield network neurons’ feedback output, the training of neurons is completed after two steps under the Hebbian learning law, and the conformity to the standard activity feature pattern is also determined. According to the probabilistic statistics on inference predictions, the proposed method, that is the neuromorphic computing of the three data fused framework, achieved the box plot for the highest lower quartile output of 95.34%, while the confusion matrix classification accuracy of the two activities was 98.98%. The results have shown that neuromorphic computing is most capable of multisensor data-fusion-based HAR. Furthermore, the proposed method can be enhanced by incorporating additional hardware signal processing in the system to enable the flexible integration of human activity data.

Modeling and Prediction of User Stability and Comfortability on Autonomous Wheelchairs With 3-D Mapping

Traditional manual wheelchairs have a fixed seat with no movement or angle adjustment, which can seriously affect the user's comfort and greatly limit user experience. However, the electric wheelchair relies on strong intelligence and automatic features; it can not only realize the multidegree freedom adjustment of the human body and the seat but also has a rich and powerful man–machine control interface, which greatly facilitates and improves the user experience. This study upgraded a Permobil C400-powered wheelchair with multisensor data fusion technology to enrich its terrain recognition, tipping stability, and comfortability prediction. The tipping stability modeling of the wheelchair dummy system is carried out using multibody dynamics and vibration mechanics to obtain the tipping stability limit and the comfort evaluation of the wheelchair vibration acceleration on the human body during travel. Based on the elevation mapping method, the wheelchair can estimate the terrain from the local point of view at any point in time. At the same time, the RGB-D depth camera is connected to the robot operating system (ROS) system, and the open-source algorithm package RTAB-MAP is used to complete the MAP construction and collect the 3-D point-cloud terrain data. Then, the real 3-D terrain files are generated through the point-cloud stitching technology for stability simulation of the wheelchair–human system. The tipping stability and comfort indexes of the wheelchair–human system when passing over different physical terrains can be obtained. The experimental results show that the IMU data located on the human chest agree well with the simulation analysis data and are suitable for a variety of complex real-terrain conditions, verifying the accuracy of the wheelchair–human system dynamics model and the feasibility of the simulation analysis process. Thus, this modeling and simulation method can predict wheelchair stability and user comfortability well and ensure a high-performance experience.

Multi-sensor data fusion technology for the early landslide warning system

Multi-sensor data fusion technology for the early landslide warning system

Fuzzy Dynamic Obstacle Avoidance Algorithm for Basketball Robot Based on Multi-Sensor Data Fusion Technology

This paper uses multi-sensor data fusion technology to design and develop a multi-sensor based basketball robot’s target recognition and positioning system in an unknown environment. We establish the movement model of the basketball robot, and combine the hardware configuration of the basketball robot, analyse the methods of speed control, position control, and direction control, propose a fuzzy obstacle avoidance strategy in the basketball robot movement process, and simulate the obstacle avoidance strategy. The simulation results show that the proposed fuzzy obstacle avoidance strategy is effective. Carry out the experiment of basketball robot target calibration, build the basketball robot monitoring system, and realize data transmission and real-time image processing. Experiments show that the basketball robot can achieve target recognition and positioning tasks in an unknown environment (obstacle comparison rules). It proves the effectiveness and robustness of the proposed following algorithm. It illustrates the effectiveness of the system designed in this paper.

Multi-sensor time synchronization algorithm based on Gaussian process regression

In order to address the problem of inconsistent sampling frequency and accuracy of each sensor in multi-sensor data fusion technology, the algorithm of Gaussian Process Regression (GPR) is proposed for time alignment, and in the scenario where the target motion model is uniform linear motion and uniform turning motion, the simulation is compared and analyzed respectively. The simulation results show that the proposed algorithm can effectively improve the accuracy of time synchronization.

Analysis of the Effect of Branched Chain Amino Acids on Muscle Health Information of Swimmers Based on Multisensor Fusion and Deep Learning.

Swimmers must fully mobilize the muscles of the whole body during exercise, and it is necessary to study the protection of swimmers from muscle damage. Now, muscle damage is increasing year by year, and more athletes are affected. Therefore, studying the causes of muscle injuries and exploring more effective treatments have become important research topics in the field of sports medicine. This study is mainly based on deep learning to analyze the protective effect of branched-chain amino acids on swimming athletes' muscle injury. Due to the complex and changeable environment and the interference of unknown factors, a single sensor cannot meet the needs of obtaining information. Therefore, people have developed the technology of multisensor information fusion to obtain enough information. Multisensor data fusion technology can synthesize the information of each sensor and then obtain more comprehensive and accurate decision-making information. This study is mainly based on multisensor fusion and deep learning to analyze the impact of branched chain amino acids on Swimmers' muscle health information. Finally, two experiments were designed in this article. The first experimental result showed that the pain level of the experimental group who took BCAA supplements was 19% lower than that of the control group that did not take the BCAA supplement within three days after exercise. The results of the second experiment show the following: after exercise, the creatine kinase activity value of the experimental group taking BCAA supplement was 4.38 ± 1.45, and the creatine kinase activity value of the control group taking placebo was 5.42 ± 2.12. It proves that BBCA can protect muscle damage by reducing the activity of creatine kinase.

Multisensor Data Fusion System for Wushu Sanda Teaching in Higher Education Institutions

Classical data fusion methods can be roughly divided into two categories: one is based on random methods, such as weighted average, least squares, Kalman filtering, Bayesian estimation, DS evidence inference, and production rules; the other is based on artificial intelligence. Intelligent methods include fuzzy logic reasoning, artificial neural networks, and support vector machine methods. This paper introduces, analyzes, and summarizes the advantages and disadvantages of the above methods, as well as the level of use of these methods, and then introduces the current trend of data fusion technology toward the mixed development of multiple methods. This article is aimed at carrying out a research on the innovation of Wushu Sanda teaching mode in colleges and universities based on multisensor data fusion technology. This article puts forward the innovative theory of youth sports training in Sanda in the new era, combined with the current status of physical education in Chinese colleges and universities, and analyzes the practicality of the theory and the effect of system integration. This article accelerates reforms and collects data from four aspects: the policy system at the macro level, the integrated governance system at the meso level, the teacher service system at the micro level, and the digital industry system at the functional innovation level, and proposes innovative strategies for youth sports training and teaching in the new era. The results of the study show that people with 5.99 are more interested in the World Cup, accounting for the largest proportion, while the number of people who are interested in martial arts is the least, only 1.63. The innovative theory of youth sports training in Sanda in the new era improves the training effect of students by 60% compared with ordinary college Sanda training. It can to a large extent protect students from reluctant exercises that do not receive physical education in traditional teaching. Physiologically, it has unique advantages for the development of students. The teaching innovation model of this article can effectively solve the problems of physical education organization difficulty.

Design of Intelligent Perception Module Based on Wireless Sensor Network and Basketball Sports Attitude

In recent years, along with microelectromechanical (MEMS) technique and wireless body area network (WBAN), wearable health monitoring systems have emerged. With sustainability posture detection technique, people pay more and more attention to mankind posture detection. Human posture detection technique has been widely used in medical, film and television, industry, sports, and other fields. Motion capture is a technique for measuring the motion of moving objects in three-dimensional space with great accuracy. It is the most efficient method for producing computer 3D animation and collecting human motion data. The use of motion capture systems in animation is becoming increasingly common. The problem of a single sensor having a large error in monitoring human motion and attitude is addressed. The use of multisensor data fusion technology is used to propose a human motion pattern recognition method based on data fusion of accelerometer and gyroscope. The system must effectively integrate the information of various sensors in order to achieve the goal of accuracy, timeliness, and reliability processing, and multisensor information fusion systems for various complex application objects are constantly emerging.

Application of Intelligent Sensor Algorithm in Student Management Information Fusion

In order to improve the effectiveness of college student management and promote the integration of college student management information, this paper applies intelligent sensor algorithms to student management. Moreover, this paper combines uncertainty theory with multisensor data fusion technology to establish a complete set of multisensor data processing tools for student information and provides a complete mathematical theoretical framework for the principles of student management information fusion. In addition, in view of the problem of comparing a large number of mixed data of information sources, it is necessary to transfer the information fragments obtained by each sensor to a common set so that the information fragments expressed in different sets can be integrated. Finally, this paper constructs an intelligent student management model and conducts research in combination with simulation experiments. Through simulation research, it can be known that the method proposed in this paper can effectively improve the effect of student management.

Water Dragon Boat Training Monitoring System Based on Multisensor Data Fusion Technology

With the development of science and technology, a variety of electronic devices have entered our lives, making our lives more intelligent and making our work more effective. This article is aimed at studying the application of multisensor data fusion technology to the water dragon boat training monitoring system. In that case, we can analyze the various physical indicators of dragon boat athletes based on the data reflected by these sensors, when they can reach their physical limits and can perform in the best state to obtain the best results. The sensor is used to decompose the relevant data of each part of the athlete’s limbs. This step is based on the image and understands the maximum value of the data to adjust the training goal. This article proposes some data fusion algorithms, using Kalman filter method, Bayesian estimation method, and DS evidence theory algorithm to compare data fusion systems, through the comparison to find the best fusion accuracy, and then get the most suitable method is then applied to this water dragon boat monitoring system to enhance the training efficiency of dragon boat athletes. The experimental results in this paper show that when the value of the parameter increases from 0.97 to 2.5, the average classification accuracy of the k -NN classifier decreases from 0.97 to 0.4, and the accuracy of the fusion results of the three fusion rules is also reduced correspondingly, but in this paper proposed, RP fusion rule still has better performance than the other two fusion rules. When the classifier is k -NN, the three fusion rules increase with the number of sensors, and the accuracy of the fusion results is correspondingly improved. However, the final fusion accuracy obtained by the RP fusion rule proposed in this paper is always better than NB integration rules, and WMV integration rules are higher. Through these analyses, a training program that is most suitable for dragon boat athletes can be worked out, so that the athletes will not be useless. Multisensor data fusion technology brings great convenience to water dragon boat training and can provide more reasonable and accurate data to explore a practical way on the basis of ensuring the safety of personnel.

Saliency area detection algorithm of electronic information and image processing based on multi-sensor data fusion

Researched in the 1980s, multi-sensor data convergence has become a hot issue. Not only does it differ from general signal processing, or single to multiple sensor surveillance and measurement, on the other hand, it is a higher level of integrated decision-making processes based on multiple sensor measurement outcomes, this paper is based on the study of the saliency area detection algorithm of electronic information and image processing based on multi-sensor data fusion, based on the improved FT algorithm and LC algorithm using multi-sensor data fusion technology, a new LVA algorithm is proposed, and these three algorithms are evaluated in an all-round way through various algorithm evaluation indicators such as PR curve, PRF histogram, MAE index, and recognition image rate. The research results show that the LVA algorithm proposed in this paper improves the detection rate of saliency maps by 5–10%.

Intelligent Water Quality Monitoring System Based on Multi-Sensor Data Fusion Technology

The intelligent water quality monitoring system takes the single chip microcomputer STM32F103C8T6 as the control core to collect signals of each sensor module and converts the collected parameters into effective digital signals by using the internal analog-to-digital converter. The data gathered by the acquisition center is sent to the analysis and processing center through the ZigBee module E18. In the analysis and processing center, data is fused and processed by the single chip microcomputer STC12C5A60S2. The data after fusion is sent to the monitoring management center through the GPRS module SIM800C. For improving the monitoring precision of the system, multi-level data fusion algorithms are used. In the data layer, abnormal values are deleted by abnormal data detection method, and the median average filtering method is used to fuse the data; the algorithm based on weighted estimation fusion is used in the feature layer; the fuzzy control fusion algorithm is used in the decision.

Research on Data Fusion Technology of Mobile Intelligent Robot Based on Internet of Things

With the development of the Internet of Things, the increasing variety of sensors and the rapid development of sensor technology, multi-sensor data fusion technology has received more and more attention. And because of its strong survivability in solving problems such as detection, tracking and identification, it can enhance the detection performance, credibility and reliability of the system, improve the accuracy of measurement data, and expand the time and space coverage of the system. Multi-sensor The application of data fusion in various fields such as military, agriculture, and industry is becoming more and more frequent. Among them, the application of data fusion technology in the field of robotics research is also in a stage of rapid development, especially in mobile robots, the application of data fusion technology is more extensive. This paper analyses the multi-sensor data fusion technology used in existing mobile robots, and conducts reasonable design and research on the development of data fusion technology in the field of intelligent mobile robot research.

Practical Moving Target Detection in Maritime Environments Using Fuzzy Multi-sensor Data Fusion

As autonomous ships become the future trend for maritime transportation, it is of importance to develop intelligent autonomous navigation systems to ensure the navigation safety of ships. Among the three core components (sensing, planning and control modules) of the system, an accurate detection of target ships’ navigation information is critical. Within a typical maritime environment, the existence of sensor noises as well as the influences generated by varying environment conditions largely limit the reliability of using a single sensor for environment awareness. It is therefore vital to use multiple sensors together with a multi-sensor data fusion technology to improve the detection performance. In this paper, a fuzzy logic-based multi-sensor data fusion algorithm for moving target ships detection has been proposed and designed using both AIS and radar information. A two-stage fuzzy logic association method has been particularly developed and integrated with Kalman filtering to achieve a computationally efficient performance. The effectiveness of the proposed algorithm has been tested and validated in simulations where multiple target ships are transiting with complex movements.

Intelligent Environmental Monitoring System Based on Multi-Sensor Data Technology

Using multi-sensor data fusion technology, ARM technology, ZigBee technology, GPRS, and other technologies, an intelligent environmental monitoring system is studied and developed. The SCM STC12C5A60S2 is used to collect the main environmental parameters in real time intelligently. The collected data is transmitted to the central controller LPC2138 through the ZigBee module ATZGB-780S5, and then the collected data is transmitted to the management computer through the GPRS communication module SIM300; thus, the real-time processing and intelligent monitoring of the environmental parameters are realized. The structure of the system is optimized; the suitable fusion model of environmental monitoring parameters is established; the hardware and the software of the intelligent system are completed. Each sensor is set up synchronously at the end of environmental parameter acquisition. The method of different value detection is used to filter out different values. The authors obtain the reliability of the sensor through the application of the analytic hierarchy process. In the analysis and processing of parameters, they proposed a new data fusion algorithm by using the reliability, probability association algorithm, and evidence synthesis algorithm. Through this algorithm, the accuracy of environmental monitoring data and the accuracy of judging monitoring data are greatly improved.

Research on Multi-sensor Data Fusion Technology

Multi-source sensor information fusion mainly collects various types of information from multiple independent decentralized sensors. Due to the different types of information, specific combinations of time and events are required to make the collected information more advanced and useful information. From the level of fusion, it will be merged from the three levels of data set, feature level and decision-making level. For different practical problems, it is necessary to use a certain level of fusion or a certain two levels of fusion according to the situation, so as to obtain the most optimal integration scheme.

RETRACTED: The application of multi-sensor target tracking and fusion technology to the comprehensive early warning information extraction of landslide multi-point monitoring data

• Application of multi-sensor target tracking fusion technology. • Proposes a spatiotemporal registration method and Ensemble Kalman Filte. • The test simulation is carried out by selecting 30 monitoring data. Landslide disasters are the second largest geological disaster activity in addition to earthquake disasters. They cause economic losses of up to several billion yuan each year, resulting in thousands of deaths and injuries. Therefore, it is important to monitor and alert landslide disasters. Significance. In addition, after the occurrence of landslides, a large amount of deposits will be created, which will further breed mudslides. The ability to qualitatively and quantitatively extract landslide information at the first time is particularly important for disaster prevention, mitigation, and disaster management. Therefore, the extraction of landslide information is a significant measure. At the same time, due to the limitations of sensors, how to organically fuse data from multiple sensors to achieve tracking performance that cannot be achieved by a single sensor has become the focus of research and attention on multi-sensor data fusion technology in the field of target tracking. Based on the above background, the research content of this paper is the application of multi-sensor target tracking fusion technology to achieve comprehensive warning information extraction of landslide multi-point monitoring data. This paper proposes a spatiotemporal registration method and Ensemble Kalman Filter (EnKF) for target tracking Algorithm, and the prediction model is established through optimization problem and target tracking. Finally, the test simulation is carried out by selecting 30 monitoring data of a monitoring point of a new construction project as the detection value. The experimental results show that when the moving target model is unknown in actual work, the polynomial. The order of the fitting and the boundary conditions of the spline fitting are not easy to give, but you can optionally use the sensor measurement data in the middle part and then use the spline fitting algorithm to obtain a better time registration effect; In terms of spatial registration, in the Z-axis direction, the measurement error of sensor A and sensor B is large, and the maximum value exceeds 2000 m. The maximum value of sensor A error between the measured values and theoretical values of radar A and B after registration is about 1000 m, which is the Z axis direction, and the maximum error of sensor B is about 1000 m, which is also the Z axis direction. The error has been significantly reduced. EnKF is better than the UKF algorithm under non-Gaussian noise conditions. The PF algorithm itself is suitable for nonlinear and non-Gaussian systems and has the best tracking performance.