Multi-sensor Information Fusion Algorithm Research Articles

Interference fading suppression with fault-tolerant Kalman filter in phase-sensitive OTDR

A multi-sensor information fusion algorithm based on fault-tolerant Kalman filter is proposed in phase-sensitive optical time-domain reflectometer (Φ-OTDR) system, for achieving fading-free distributed vibration sensing. Firstly, a fault-tolerant dual-core complementary array model is designed. The Rayleigh scattering signal denoising, and vibration existence judgment of localization points are carried out to obtain the differentiated frequency demodulation results of the sensing points of the dual-core fiber array. Then a fault-tolerant control strategy is used to determine the sensor weight coefficients and vibration judgment coefficients during data fusion processing, and array data fusion is carried out based on time series data using Kalman filter to realize error value identification and filling. The advantage of this method is the combination of redundant data in a complementary way to improve the system stability. The frequency response ranges from 10 Hz to 2400 Hz and the localization accuracy is 98.33%. The influence of key parameters on the frequency demodulation performance of fault-tolerant Kalman filter is discussed, and a standard deviation of 14.6 Hz and an average error of 7.6 Hz are obtained. The demodulation frequency data matrix obtained by the classical demodulation method has a demodulation error probability of 89.18%, which proves the widespread existence of demodulation errors in vibration signals. The fusion error of demodulation frequency is reduced to 0.25 Hz, the frequency demodulation accuracy reaches 100%, and the demodulation error caused by interference attenuation can be completely eliminated. This system based on fault-tolerant Kalman filter has the characteristics of simple multiplexing structure, interference fading resistance and stable demodulation performance.

Multi-sensor information fusion localization of rare-earth suspended permanent magnet maglev trains based on adaptive Kalman algorithm.

Since the positioning accuracy of sensors degrades due to noise and environmental interference when a single sensor is used to localize a suspended rare-earth permanent magnetically levitated train, a multi-sensor information fusion method using multiple sensors and self-correcting weighting is proposed for permanent magnetic levitated train localization. A decay memory factor is introduced to reduce the weight of the influence of historical measurement data on the fusion estimation, thus enhancing the robustness of the fusion algorithm. The Kalman filtering results suffer from inaccuracy when process noise is present in the system. In this paper, we use a covariance adaptive scheme that replaces the prediction step of the Kalman filter with covariance. It uses the covariance adaptive scheme to search the posterior sequence online and reconstruct the prior error covariance. Since the process noise covariance is not used in the new adaptive scheme, the negative impact of the mismatch noise statistics is greatly reduced. Simulation and experimental results show that the use of multi-sensor information fusion and covariance adaptive Kalman algorithm has significant advantages in terms of adaptability, accuracy and simplicity.

Fault diagnosis of the hydraulic valve using a novel semi-supervised learning method based on multi-sensor information fusion

Hydraulic systems are usually applied in large and complex engineering fields. For hydraulic systems or components in operation, it is difficult to obtain fault data with fault labels due to the high engineering cost. Therefore, a semi-supervised learning (SSL) method based on multi-sensor information fusion is proposed to obtain valuable pseudo label data to diagnose faults of the hydraulic directional valve in operation. In this method, the classification model is trained from a small amount of data with fault labels, thus generating pseudo labels for a large amounts of unmarked data. The contribution of this article is that a multi-sensor fusion algorithm is designed to obtain pseudo labels with high confidence, and an adaptive threshold model similar to generative countermeasure network is designed to intelligently generate thresholds for selecting pseudo labels instead of human intervention. Theoretical and experimental results show that the multi-sensor information fusion algorithm can obtain high confidence pseudo tags, the adaptive threshold model can screen effective pseudo tag samples by generating appropriate thresholds for accelerating the convergence of the classification model. In the hydraulic valve fault diagnostic test, after five iterations, the average diagnosis accuracy of this method can reach 99.72% and 99.00% respectively for different types of hydraulic valves in different engineering fields. This provides a new idea for developing intelligent hydraulic directional valve with self fault diagnosis function.

Reliability Analysis of Multisensor Fusion Technology in Disaster Nursing Application

The harm and impact of various natural disasters are becoming more and more obvious. Due to the high suddenness of natural disasters and the low efficiency of information sharing, our stress system will still not be able to respond to disasters in time. Therefore, facing the current complex natural environment and the diversity of disasters, we urgently need a practical and comprehensive online collaborative natural disaster protection system. Therefore, this paper proposes a disaster protection application system based on a multisensor information fusion algorithm. Firstly, the software platform of the natural disaster protection system based on multisensor is constructed; the hardware platform of multisensor disaster acquisition is designed. An information fusion algorithm based on Kalman joint filter is proposed. The main problems of joint filter are (1) improving the filtering accuracy, (2) improving the fault-tolerant performance of the filter, and (3) optimizing the fusion algorithm and reducing the amount of calculation and data communication, so as to facilitate real-time implementation of the algorithm. Combined with DS evidence theory, BP neural network, and support vector mechanism, a comprehensive diagnosis model is established. The model uses the preliminary diagnosis results of the BP neural network and support vector machine to carry out the BPA of DS evidence theory, which avoids the blindness caused by human operation. The simulation results show that the comprehensive diagnosis model can significantly improve the prediction accuracy of disasters, solve the uncertainty and fuzziness of diagnosis results of a single diagnosis model, and provide a new method for disaster protection. The results of the BP neural network in the disaster nursing system are fused at the decision level, the results show that the distribution probability corresponding to the fusion disaster category is greater than the threshold value of 0.8, and the diagnosis result can be given immediately. The diagnosis result is completely consistent with the actual disaster category.

Research on risk assessment method of steel catenary riser based on information fusion

The Steel Catenary Riser (SCR) is an essential component connecting the subsea production system and the offshore floating platform and SCR malfunction leads to disastrous consequences. In order to ensure operation safety, the operating company monitors SCR motion response in real time by installing sensors. This paper aims to develop a multi-sensor information fusion algorithm for SCR risk assessment. The algorithm generates basic probability assignment (BPA) function based on known information. Then, the intuitionistic fuzzy multi-attribute decision matrix is constructed by BPA to evaluate the dynamic reliability of the sensor. Finally, the focal element credibility is calculated to reduce the conflict between evidences, and the improved Desert-Smarandache (DSm) theory fusion rule is utilized to evaluate the SCR risk through decision fusion. The feasibility of this method is verified by the case study. The corresponding risk control strategy is also provided.

Multisensor Information Fusion-Assisted Intelligent Art Design under Wireless Virtual Reality Environment

Under the background of intelligent technologies, art designers need to use information technology to assist the design of art factors and fully realize the integration of art design and information technology. Multisensor information fusion technology can more intuitively and visually carry out a more comprehensive grasp of the objectives to be designed, maximize the positive effects of art design, and achieve its overall optimization and can also help art designers get rid of the traditional monolithic and obsolete design concepts. Based on multisensor information fusion technology under wireless virtual reality environment, principles of signal acquisition and preprocessing, feature extraction, and fusion calculation, to analyze the information processing process of multisensor information fusion, conduct the model construction and performance evaluation for intelligent art design, and propose an intelligent art design model based on multisensor information fusion technology, we discuss the realization of multisensor information fusion algorithm in intelligent art design and finally carry out a simulation experiment and its result analysis by taking the environment design of a parent-child restaurant as an example. The study results show that using multisensor information fusion in the environmental design of parent-child restaurant is better than using a single sensor for that; at the same time, using force sensors has a better environmental design effect than using vibration sensors. The multisensor information fusion technology can automatically analyze the observation information of several sources obtained in time sequence under certain criteria and comprehensively perform information processing for the completion of the decision-making and estimation tasks required for intelligent art design.

Research on multi-sensor information fusion and intelligent optimization algorithm and related topics of mobile robots

Research on mobile robots began in the late 1960s. Mobile robots are a typical autonomous intelligent system and a hot spot in the high-tech field. They are the intersection of multiple technical disciplines such as computer artificial intelligence, robotics, control theory and electronic technology. The product not only has potentially very attractive application value and commercial value, but the research on it is also a challenge to intelligent technology. The development of mobile robots provides excellent research for various intelligent technologies and solutions. This dissertation aims to study the research of multi-sensor information fusion and intelligent optimization methods and the methods of applying them to mobile robot related technologies, and in-depth study of the construction of mobile robot maps from the perspective of multi-sensor information fusion. And, in order to achieve this function, combined with autonomous exploration and other related theories and algorithms, combined with the Robot Operating System (ROS). This paper proposes the area equalization method, equalization method, fuzzy neural network and other methods to promote the realization of related technologies. At the same time, this paper conducts simulation research based on the SLAM comprehensive experiment of the JNPF-4WD square mobile robot. On this basis, the high precision and high reliability of robot positioning are further realized. The experimental results in this paper show that the maximum error of the X-axis and Y-axis, FastSLAM algorithm is smaller than EKF algorithm, and the improved FASTSALM algorithm error is further reduced compared with the original FastSLAM algorithm, the value is less than 0.1.

Target Recognition Algorithm Based on Optical Sensor Data Fusion

Optical sensor data fusion technology is a research hotspot in the field of information science in recent years, which is widely used in military and civilian fields because of its advantages of high accuracy and low cost, and target recognition is one of the important research directions. Based on the characteristics of small target optical imaging, this paper fully utilizes the frontier theoretical methods in the field of image processing and proposes a small target recognition algorithm process framework based on visible and infrared image data fusion and improves the accuracy as well as stability of target recognition by improving the multisensor information fusion algorithm in the photoelectric meridian tracking system. A practical guide is provided for the solution of the small target recognition problem. To facilitate and quickly verify the multisensor fusion algorithm, a simulation platform for the intelligent vehicle and the experimental environment is built based on Gazebo software, which can realize the sensor data acquisition and the control decision function of the intelligent vehicle. The kinematic model of the intelligent vehicle is firstly described according to the design requirements, and the camera coordinate system, LiDAR coordinate system, and vehicle body coordinate system of the sensors are established. Then, the imaging models of the depth camera and LiDAR, the data acquisition principles of GPS and IMU, and the time synchronization relationship of each sensor are analyzed, and the error calibration and data acquisition experiments of each sensor are completed.

Design and Implementation of High-Skilled Talent Information Management System Based on Multisensor Information Fusion

With the rapid development of science and technology, the global network and informatization process is changing people’s lives, and it has also brought profound changes to the management of enterprises. Traditional management still relies heavily on offline management and paper delivery of information. The traditional management model in the past cannot meet the needs of the continuous development of enterprises. The purpose of this article is to design a high-skilled talent information management system to meet the needs of users and enterprises. This paper combines the BP multisensor information fusion algorithm to screen and process the data and designs a multisensor information fusion-based competency evaluation model for highly skilled talents. A high-skilled talent information management system has been established with modules such as basic personnel information, skills identification information, high-skilled talent management, competence, key research teams, common reports, comprehensive query, and system maintenance. In the test of the system’s usability, 156 users rated the system above 3 points, accounting for 78% of the survey population, indicating that the system has a certain ease of use. This system realizes the electronicization of the basic information of skilled personnel, which plays an important role in regulating the professional management of skilled personnel and improving the management efficiency of skilled personnel.

Interoperability technology of sports health monitoring equipment based on multi-sensor information fusion

Nowadays, the pace of society is accelerating, people’s living standard has been greatly improved compared with the past, and we pay more and more attention to the real-time, flexibility, and intelligence of medical services. Information fusion technology is the study of how to effectively and comprehensively utilize multi-sensor information to overcome information imperfections and uncertainties. In recent years, it has become a hotspot for research, but with the development of communication technology, electronic technology, and computers, the form of information becomes complicated and diverse in science and technology such as technology. In this case, the information obtained by a single sensor is always imperfect, so multiple sensors must be used to detect the target from multiple angles. Obtaining more dimensional information about the target and fusing it to ensure the integrity and accuracy of the information helps to accurately determine the physiological health of the human body. This paper analyzes the multi-sensor information fusion technology in detail, discusses the basic principle of multi-sensor information fusion, fusion process, fusion level, architecture and general fusion algorithm, constructs a sensor information fusion technology and its application model in biomedical detection, from the application framework, control structure of multi-sensor integrated system, information fusion, etc. The current situation of multi-sensor information fusion technology is studied in several aspects. An interoperability technology of sports health monitoring equipment based on multi-sensor information fusion is constructed. The experimental results show that the experimental efficiency is improved by 20% after using the application model in this paper, which has certain practicability.

Design of Intelligent Robot Platform based on Multi-sensor Fusion

The mobile robot adapts to the more complicated indoor and outdoor environments, and can expand its scope of application. In order to reduce the influence of the cumulative error caused by navigation in complex environments, the indoor mobile robot that combines Inertial Measurement Unit (IMU) and encoder fusion is designed and implemented. In view of the limitations of the traditional single lidar scheme, a Multi-sensor Fusion scheme is proposed to achieve indoor map construction, path planning, multi-point navigation and other functions, and a MSIF KartoSLAM (Multi-sensor Information Fusion) algorithm is proposed, which combines the KartoSLAM algorithm and Multi-sensor information to achieve map construction in complex environments. Through comprehensive testing in the indoor environment, the results show that the Multi-sensor Fusion scheme is superior to the traditional single lidar scheme, and can achieve higher accuracy in mapping and navigation. At the same time, the robot platform can also be combined with the Internet of Things technology and integrated into intelligent housing system.

Nuisance alarm rate reduction using pulse-width multiplexing [formula omitted]-OTDR with optimized positioning accuracy

High nuisance alarm rates and missing alarm rates in phase-sensitive optical time-domain reflectometer (ϕ-OTDR) greatly restrict their practical application. In this paper, an asynchronous sampling pulse width multiplexing ϕ-OTDR is proposed to reduce both of these rates. Intersection and inclusion relationships among multiple positioning intervals are used to optimize the positioning accuracy. We construct an eight-pulse-width multiplexed ϕ-OTDR system and adopt a multisensor information fusion algorithm to verify the feasibility of the method. The experimental results show that this method can reduce the missing alarm rate by 93% and the number of nuisance alarms by 97%. The average positioning accuracy of the system is 21.4 m, which is equivalent to the spatial resolution level corresponding to the minimum pulse width in the optical pulse sequence. The positioning accuracy of some groups was as low as 6.4 m, far less than 20m. The proposed method provides a simple and feasible new approach for reducing nuisance alarm and missing alarm rates and optimizing the positioning accuracy of ϕ-OTDR.

Multi-sensor Information Fusion Method Based on BP Neural Network

This study aims to solve the problem of multi-sensor information fusion, which is a key issue in the multi-sensor system development. The main innovation of this study is to propose a novel multi-sensor information fusion algorithm based on back propagation neural network and Bayesian inference. In the proposed algorithm, a triple is defined to represent a probability space; thereafter, the Bayesian inference is used to estimate the posterior expectation. Finally, we construct a simulation environment to test the performance of the proposed algorithm. Experimental results demonstrate that the proposed algorithm can significantly enhance the accuracy of temperature detection after fusing the data obtained from different sensors.

AUV Positioning Method Based on Tightly Coupled SINS/LBL for Underwater Acoustic Multipath Propagation.

This paper researches an AUV (Autonomous Underwater Vehicle) positioning method based on SINS (Strapdown Inertial Navigation System)/LBL (Long Base Line) tightly coupled algorithm. This algorithm mainly includes SINS-assisted searching method of optimum slant-range of underwater acoustic propagation multipath, SINS/LBL tightly coupled model and multi-sensor information fusion algorithm. Fuzzy correlation peak problem of underwater LBL acoustic propagation multipath could be solved based on SINS positional information, thus improving LBL positional accuracy. Moreover, introduction of SINS-centered LBL locating information could compensate accumulative AUV position error effectively and regularly. Compared to loosely coupled algorithm, this tightly coupled algorithm can still provide accurate location information when there are fewer than four available hydrophones (or within the signal receiving range). Therefore, effective positional calibration area of tightly coupled system based on LBL array is wider and has higher reliability and fault tolerance than loosely coupled. It is more applicable to AUV positioning based on SINS/LBL.

AUV Underwater Positioning Algorithm Based on Interactive Assistance of SINS and LBL.

This paper studies an underwater positioning algorithm based on the interactive assistance of a strapdown inertial navigation system (SINS) and LBL, and this algorithm mainly includes an optimal correlation algorithm with aided tracking of an SINS/Doppler velocity log (DVL)/magnetic compass pilot (MCP), a three-dimensional TDOA positioning algorithm of Taylor series expansion and a multi-sensor information fusion algorithm. The final simulation results show that compared to traditional underwater positioning algorithms, this scheme can not only directly correct accumulative errors caused by a dead reckoning algorithm, but also solves the problem of ambiguous correlation peaks caused by multipath transmission of underwater acoustic signals. The proposed method can calibrate the accumulative error of the AUV position more directly and effectively, which prolongs the underwater operating duration of the AUV.

Hardware and Software Design of Mine Environmental Monitoring System

A mine environmental monitoring system which is composed of hardware and user software is developed. The functions of the hardware are monitoring the complex coal mine environmental factors, uploading monitoring data and controlling the designed environmental equipment. The user software is programmed by Java and it stores the uploaded data in a database, draws curves, makes predictions and so on. Through analyzing and processing the uploaded data, the user software can makes decisions and sends control commands to the hardware, thus the designed environmental equipments change the environment. Additionally, an improved multi-sensor information fusion algorithm is proposed to improve the stability and accuracy of this system. Finally, an experiment is carried out to test our system.

Detection and Control of Flexible Peristaltic Pipeline Robot in Elbow

In order to make the peristaltic pipeline robot go through elbow quickly and efficiently, a multi-sensor information fusion algorithm based on relation matrix and fuzzy logic control is proposed. In this method, relation matrix is adopted to fuse the information from ultrasonic sensors and infrared sensors, while fuzzy neural network is employed to make walking strategy and remove obstructions. Simulation and platform testing are designed to verify the effectiveness of this method. The test results show that this method can significantly improve distance measuring accuracy and walking efficiency.

Design of MEMS Wireless Air-Mouse System Based on MEMS Technology

In this paper, we study the MEMS wireless air-mouse system(MWAMS), which based on three-axis gyroscope、three-axis accelerometer、magnetometer and wireless RF technology. It makes use of MEMS motion sensing technology、information fusion algorithm and DCM complementary filter algorithm to sensing air-mouse's motion. This paper presents a new multi-sensor optimal information fusion and mapping algorithm. Compared to the traditional algorithm, this new algorithm based on MEMS technology not only has higher performance-higher location accuracy and better smoothness, but also has lower power consumption.

Uncertainty Analysis of Integrated Navigation Model for Underwater Vehicle

In this study, to reduce information uncertainty of integrated navigation model for underwater vehicle, we present a multi-sensor information fusion algorithm based on evidence theory. The algorithm reduces attribution by rough set in order to acquire simplified ELMAN neural network and improve basic probability assignment. And then it uses improved D-S evidence to deal with the inaccuracy and fuzzy information, make the final decision. The simulation example shows feasibility and effectiveness of the algorithm.

Dynamic Position Detection Method for Large-Diameter Rotary Shaft Based on MIFA-AFKF

The dynamic position variables of large-diameter rotary shaft are not easy to measure precisely or cheaply, however, the accuracy of dynamic position is of great significance to the movement controlling of key rotary shaft. In this paper, a novel real-time detection method is presented, which can be used to estimate the dynamic position of the large-diameter rotary shaft rotating with constant velocity, constant acceleration or variable acceleration. In order to estimate the position variables, multi-sensor information fusion algorithm based on adaptive fading Kalman filter (MIFA-AFKF), which combines conventional information fusion algorithm with an adaptive fading factor, is proposed to fuse multi grating-sensor information. The method based on MIFA-AFKF is demonstrated by Monte Carlo simulation, and also used to measure the angle position of the hollow shaft at the machine head and machine tail of a horizontal riveting machine. The two results show that the algorithm can significantly improve the dynamic position accuracy.