Information Fusion System Research Articles

A hybrid information fusion method for SINS/GNSS integrated navigation system utilizing GRU-aided AKF during GNSS outages

Abstract To enhance the performance of integrated inertial navigation system (INS) and global navigation satellite system (GNSS) during GNSS outages, this paper proposed a fusion positioning method based on predictive observation information and adaptive filter parameter. Combined with an adaptive Kalman filter (AKF) and a Gated Recurrent Unit neural network (NN) that directly relates the inertial measurement unit (IMU) output sequence to the error estimation, the hybrid information fusion system can provide effective corrections to compensate for horizontal position errors under the constraints of complex and dynamic vehicle movement data during GNSS outages. Meanwhile, the designed adaptive parameter of the integrated navigation filter can adjust the credibility of the state prediction section when the GNSS is reconnected, ensuring the system can switch rapidly between the INS/GNSS and INS/NN integrated modes. The performance of the proposed information fusion method has been experimentally validated using IMU and GNSS data collected in a vehicle navigation test conducted on a stretch of expressway. The comparison results indicate that the proposed algorithm has error suppression capabilities under various experimental constraints and demonstrates a degree of extendibility and reusability.

Predicting stroke events with a proactive fusion system: a comprehensive study on imbalance class handling in computational biomechanics

Stroke, as a critical global health concern and the second leading cause of death, occurs when blood flow to the brain is interrupted. Although machine learning has advanced in medical safety, there is limited research on stroke prediction using information fusion systems. This study presents a fusion framework that combines multiple base classifiers and a Meta classifier to improve stroke prediction performance. The research utilizes Grid Search optimized models, such as Random Forest, Support Vector Machine, K Nearest Neighbors, AdaBoost, Gradient Boosting, Light Gradient Boosting, Categorical Boosting, and eXtreme Gradient Boosting for in-depth stroke analysis. Since stroke events are rare and unpredictable, classification outcomes can be deceptive due to imbalanced data. This article examines stroke probability by comparing three data balancing methods: over-sampling, under-sampling, and tomek-link synthetic minority over-sampling (SMOTE-TL) to enhance prediction accuracy. The findings reveal that AdaBoost as a meta-classifier attains the highest performance in the fusion framework, with a peak of 88.09% Recall and 83.66% F1 score. This innovative approach provides crucial insights into stroke prediction and can be a valuable resource for strengthening intervention efforts in advanced healthcare systems.

Retracted: Study and Application on Big Data Information Fusion System Based on IoT

Retracted: Study and Application on Big Data Information Fusion System Based on IoT

Data fabric and digital twins: An integrated approach for data fusion design and evaluation of pervasive systems

Recent technological improvements have made it possible for pervasive computing intelligent environments, augmented by sensors and actuators, to offer services that support society's aims for a wide variety of applications. This requires the fusion of data gathered from multiple sensors to convert them into information to obtain valuable knowledge. Poor implementation of data fusion hinders the appropriate actions from being taken and offering the appropriate support to users and environment needs, particularly relevant in the healthcare domain. Data fusion poses challenges that are mainly related to the quality of the data or data sources, the definition of a data fusion process and evaluating the data fusion carried out. There is also a lack of holistic engineering frameworks to address these challenges. These frameworks should be able to support automated methods of extracting knowledge from information, selecting algorithms and techniques, assessing information and evaluating information fusion systems in an automatic and standardized manner. This work proposes a holistic framework to improve data fusion in pervasive systems, addressing the issues identified by means of two processes: the first of which guides the design of the system architecture and focuses on data management. It is based on a previous proposal that integrated aspects of Data Fabric and Digital Twins to solve data management and data contextualization and representation issues, respectively. The extension of the previous proposal presented here was mainly defined by integrating aspects and techniques from different well-known multi-sensor data fusion models. The previous proposal identified high-level data processing activities and was intended to facilitate their traceability to components in the system architecture. However, the previously defined stages are not completely adequate in a data fusion process and the data processing tasks to be performed in each stage are not described in detail, especially in the data fusion stages. The second process of the framework deals with evaluating data fusion systems and is based on international standards to ensure the quality of the data fusion tasks performed by such systems. This process also offers guidelines for designing the architecture of an evaluation subsystem to automatically perform data fusion evaluation in runtime as part of the system. To illustrate the proposal, a system for preventing the spread of COVID-19 in nursing homes is described that was developed using the proposed guidelines It is also illustrated by a description of how the data fusion tasks it supports are evaluated by the proposed evaluation process. The overall evaluation of the data fusion performed by this system was considered satisfactory, which indicates that the proposal facilitates the design and development of data fusion systems and helps to achieve the necessary quality requirements.

Performance evaluation of information fusion systems based on belief entropy

Information fusion systems are widely applied in many fields. However, how to quantitatively evaluate the performance of information fusion systems is still an open issue. To pioneeringly address the issue, in this paper, a performance evaluation model of information fusion systems based on Deng entropy is proposed. The proposed model quantitatively indicates the ability of evidential data fusion algorithms, including Dempster’s combination rule, average combination method, Murphy’s combination method, Yager’s combination rule, and Dubois’s combination rule, to eliminate uncertainty during the fusion process. Deng entropy serves as an indicator to characterize the uncertainty before and after fusion. We define the fusion efficiency parameter η, to numerically evaluate the performance of information fusion systems. Conflict among evidences can also be manifested in the efficiency parameter η. Several examples are presented to illustrate properties of the model. Finally, two real applications in classification are given to verify the practicability of this model.

Real-Time Information Fusion System Implementation Based on ARM-Based FPGA

In this study, an information fusion system displayed fusion information on a transparent display by considering the relationships among the display, background exhibit, and user’s gaze direction. We used an ARM-based field-programmable gate array (FPGA) to perform virtual–real fusion of this system as well as evaluated the virtual–real fusion execution speed. The ARM-based FPGA used Intel® RealsenseTM D435i depth cameras to capture depth and color images of an observer and exhibit. The image data was received by the ARM side and fed to the FPGA side for real-time object detection. The FPGA accelerated the computation of the convolution neural networks to recognize observers and exhibits. In addition, a module performed by the FPGA was developed for rapid registration between the color and depth images. The module calculated the size and position of the information displayed on a transparent display according to the pixel coordinates and depth values of the human eye and exhibit. A personal computer with GPU RTX2060 performed information fusion in ~47 ms, whereas the ARM-based FPGA accomplished it in 25 ms. Thus, the fusion speed of the ARM-based FPGA was 1.8 times faster than on the computer.

Innovative reform strategy of college music education art practice based on information fusion technology

Abstract This paper is designed to improve the efficiency and quality of teaching and learning efforts and to meet the multidimensional information needs of music educators and learners. From the perspective of information fusion technology, we build a multi-source information fusion system based on the basic system architecture. The maximum likelihood, least squares, and D-S algorithms were used to calculate and construct the sampling evaluation index equations. A survey of the teaching methods used in music teaching in colleges and universities was conducted to analyze three strategies needed for college education reform: innovative teaching methods, strengthening teachers’ music competence and quality, and establishing feedback mechanisms for college music teaching evaluation. The combined survey results show that 72% of music teachers believe their teaching skills have been greatly enhanced, while students’ willingness to take music lessons after using information integration technology has all risen to over 80%.Therefore, in the process of music teaching in colleges and universities, the integration of information integration technology can not only improve students’ interest in learning but also improve teachers’ teaching ability, which provides a certain degree of reference for the reform of music teaching in colleges and universities.

An Information Fusion System-Driven Deep Neural Networks With Application to Cancer Mortality Risk Estimate.

Next-generation sequencing (NGS) genomic data offer valuable high-throughput genomic information for computational applications in medicine. Using genomic data to identify disease-associated genes to estimate cancer mortality risk remains challenging regarding to computational efficiency and risk integration. For determining mortality-related genes, we propose an information fusion system based on a fuzzy system to fuse the numerous deep-learning-based risk scores, consider the significance of features related to time-varying effects and risk stratifications, and interpret the directional relationship and interaction between outcome and predictors. Fuzzy rules were implemented to integrate the considerations mentioned above by merging all the risk score models to achieve advanced risk estimation. The genomic data of head and neck squamous cell carcinoma (HNSCC) were used to evaluate the performance of the proposed computational approach. The results indicated that the proposed computational approach exhibited optimal ability to identify mortality risk-related genes in HNSCC patients. The results also suggest that HNSCC mortality is associated with cancer inflammatory response, the interleukin-17A signaling pathway, stellate cell activation, and the extracellular-regulated protein kinase five signaling pathway, which might offer new therapeutic targets HNSCC through immunologic or antiangiogenic mechanisms. The proposed information fusion system can promote the determination of high-risk genes related to cancer mortality. This study contributes a valid cancer mortality risk estimate that can identify mortality-related genes.

Improved decentralized GNSS/SINS/odometer fusion system for land vehicle navigation applications

Due to low cost and complementary performance advantages, global navigation satellite system (GNSS)/ strapdown inertial navigation system (SINS) integrated systems have established themselves in certain areas of land vehicle navigation. However, this integrated system cannot maintain reliable positioning solutions in challenging environments due to the inherent fragility of GNSS signals and time accumulated errors of a stand-alone SINS. To address this challenge, a multi-source information fusion system based on the decentralized system architecture and sequential Kalman filter for a land vehicle is proposed, which can fuse information from an odometer and motion aided constraints selectively and adaptively in different driving environments. Moreover, a comprehensive calibration and compensation strategy is designed to enhance the information fusion. On the one hand, a real-time calibration algorithm is designed to estimate the time-varying odometer scale factor and the misalignment between the inertial measurement unit (IMU) and vehicle body frame when GNSS signals are available. On the other hand, the forward velocity error caused by the lever arm, and the non-zero lateral velocity generated by the turning maneuver are compensated by the introduced velocity compensation method. A real car experiment in urban areas is carried out to illustrate the effectiveness of the proposed system. It shows that the proposed decentralized GNSS/SINS/odometer fusion system can maintain an average horizontal positioning root mean square error (RMSE) of 1-meter level when GNSS signals are cut off about 1-2 min. In addition, compared with the traditional centralized fusion structure, the proposed decentralized fusion structure can mitigate the horizontal positioning RMSE of the whole trajectory from 2.95 m to 0.59 m, which verifies that it can obtain better performance for the application of low-cost sensors in complex GNSS environments.

Designing Possibilistic Information Fusion—The Importance of Associativity, Consistency, and Redundancy

One of the main challenges in designing information fusion systems is to decide on the structure and order in which information is aggregated. The key criteria by which topologies are constructed include the associativity of fusion rules as well as the consistency and redundancy of information sources. Fusion topologies regarding these criteria are flexible in design, produce maximal specific information, and are robust against unreliable or defective sources. In this article, an automated data-driven design approach for possibilistic information fusion topologies is detailed that explicitly considers associativity, consistency, and redundancy. The proposed design is intended to handle epistemic uncertainty—that is, to result in robust topologies even in the case of lacking training data. The fusion design approach is evaluated on selected publicly available real-world datasets obtained from technical systems. Epistemic uncertainty is simulated by withholding parts of the training data. It is shown that, in this context, consistency as the sole design criterion results in topologies that are not robust. Including a redundancy metric leads to an improved robustness in the case of epistemic uncertainty.

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.

Study and Application on Big Data Information Fusion System Based on IoT

In our study, we first illustrated information fusion technology and Internet of Things (IoT), and then we built farmland IoT information collection platform on the basis of ZigBee technology and agricultural sensors to collect climate data including air pressure, temperature, soil water content, light intensity, and relative humidity. Finally, prediction model was used to evaluate crop growth condition. Results show that temperature increases with time and reaches the maximum at 13:00 PM. But relative humidity decreases with time and reaches the maximum at 3:30 AM. Light intensity presents a straight trend with time and reaches the maximum at 13:30 PM. CO2 concentration presents a fluctuation trend with time and reaches high point at 7:00 AM. Prediction model presented a high accuracy outcome with 99% accuracy in training data and 100% in testing set. Therefore, we can conclude that big data fusion technology on the basis of IoT has a good future in many fields excepting agriculture crop, which is also an irreversible trend.

Upper Stage Visual Inertial Integrated Navigation Method Based on Factor Graph

In the working process of the upper stage integrated navigation information fusion system, the multi-source navigation information fusion algorithm based on factor graph Bayesian estimation is used to fuse the information of inertial sensors, visual sensors and other sensors. The overall joint probability distribution of the system is described in the form of probability graph model with the dependence of local variables, so as to reduce the complexity of the system, adjust the data structure of information fusion to improve the efficiency of information fusion and smoothly switch the sensor configuration.

Information fusion and multi-classifier system for miner fatigue recognition in plateau environments based on electrocardiography and electromyography signals

Background and objectiveHuman factors are important contributors to accidents, especially human error induced by fatigue. In this study, field tests and analyses were conducted on physiological indexes extracted from electrocardiography (ECG) and electromyography (EMG) signals in miners working under the extreme conditions of a plateau environment. To provide insights into models for fatigue classification and recognition based on machine learning, multi-modal feature information fusion and miner fatigue identification based on ECG and EMG signals as physiological indicators were studied. MethodsFifty-five miners were randomly selected as field test subjects, and characteristic signals were extracted from 110 groups of ECG and EMG signals as the basic signals for fatigue analysis. We conducted principal component analysis (PCA) and grey relational analysis (GRA) on the measurement indicators. Support vector machine (SVM), random forest (RF) and extreme gradient boosting (XG-Boost) machine learning models were used for fatigue classification based on multi-modal information fusion. The area under the receiver operating characteristic (ROC) curve and the confusion matrix were used to evaluate the performance of the recognition models. ResultsThe ECG and EMG signals showed obvious changes with fatigue. The results of fatigue model identification showed that PCA feature fusion was superior to GRA feature fusion for all three machine learning approaches, and XG-Boost achieved the best performance, with a recognition accuracy of 89.47%, a sensitivity and specificity of 100%, and an AUC of 0.90. The SVM model also showed good recognition performance (89.47% accuracy, AUC=0.89). The worst performance was that of the RF model, with a recognition accuracy of only 78.95%. ConclusionsThis study shows that the physiological indexes of ECG and EMG exhibit obvious, regular changes with fatigue and that it is feasible to use SVM, RF and XG-Boost models for miner fatigue identification. The PCA fusion technique can improve the identification accuracy more than the GRA method. XG-Boost classification yields the best accuracy and robustness. This study can serve as a reference for clinical research on the identification of human fatigue at high altitudes and for the clinical study of acute mountain sickness and human acclimatization to high altitudes.

Conflict Data Fusion in a Multi-Agent System Premised on the Base Basic Probability Assignment and Evidence Distance.

The multi-agent information fusion (MAIF) system can alleviate the limitations of a single expert system in dealing with complex situations, as it allows multiple agents to cooperate in order to solve problems in complex environments. Dempster–Shafer (D-S) evidence theory has important applications in multi-source data fusion, pattern recognition, and other fields. However, the traditional Dempster combination rules may produce counterintuitive results when dealing with highly conflicting data. A conflict data fusion method in a multi-agent system based on the base basic probability assignment (bBPA) and evidence distance is proposed in this paper. Firstly, the new bBPA and reconstructed BPA are used to construct the initial belief degree of each agent. Then, the information volume of each evidence group is obtained by calculating the evidence distance so as to modify the reliability and obtain more reasonable evidence. Lastly, the final evidence is fused with the Dempster combination rule to obtain the result. Numerical examples show the effectiveness and availability of the proposed method, which improves the accuracy of the identification process of the MAIF system.

Soil moisture map construction by sequential data assimilation using an extended Kalman filter

Microwave remote sensors mounted on center pivot irrigation systems provide a feasible approach to retrieve soil water information, in the form of water content maps, for the implementation of closed-loop irrigation. Major challenges such as significant time delays in the soil water measurements, the inability of the sensors to provide soil water information in instances where the center pivot is stationary, and the inability of the sensors to provide soil water information in the root zone reduce the usability of these water content maps in the effective implementation of closed-loop irrigation. In this paper, we seek to address the aforementioned challenges and consequently describe a water content map construction procedure that is suitable for the implementation of closed-loop irrigation. Firstly, we use the cylindrical coordinate version of the Richards equation, also known as the field model, to model an agro–hydrological system that is equipped with a center pivot irrigation system. Secondly, soil water content observations retrieved from the microwave sensors are assimilated into the field model using the extended Kalman filter to form an information fusion system, which will provide frequent soil water estimates and predictions in the form of water content maps. The utility of the proposed information fusion system is first investigated with simulated microwave sensor measurements. The information fusion system is then applied to a real large-scale agriculture field where we demonstrate its ability to address the forgoing challenges. Three performance evaluation criteria are used to validate the soil water estimates and predictions provided by the proposed information fusion system.

VGF-Net: Visual-Geometric fusion learning for simultaneous drone navigation and height mapping

The drone navigation requires the comprehensive understanding of both visual and geometric information in the 3D world. In this paper, we present a Visual-Geometric Fusion Network (VGF-Net), a deep network for the fusion analysis of visual/geometric data and the construction of 2.5D height maps for simultaneous drone navigation in novel environments. Given an initial rough height map and a sequence of RGB images, our VGF-Net extracts the visual information of the scene, along with a sparse set of 3D keypoints that capture the geometric relationship between objects in the scene. Driven by the data, VGF-Net adaptively fuses visual and geometric information, forming a unified Visual-Geometric Representation. This representation is fed to a new Directional Attention Model (DAM), which helps enhance the visual-geometric object relationship and propagates the informative data to dynamically refine the height map and the corresponding keypoints. An entire end-to-end information fusion and mapping system is formed, demonstrating remarkable robustness and high accuracy on the autonomous drone navigation across complex indoor and large-scale outdoor scenes.

Research on Simulation Experiment of Underwater Cluster Multi-source Information Fusion

Intelligent cluster information fusion technology is one of the key technologies to solve systematic cooperative operations in underwater manned and unmanned cooperative operations clusters. In this study, aiming at the multi-node hybrid multi-level hierarchical information fusion architecture and global optimization association algorithm based on cluster formation detection, the test scheme and evaluation method of multi-level information fusion are proposed, and the cluster information fusion simulation test system including sonar system, simulation deduction and evaluation system and multi-source information fusion system is constructed. For the first time, the system comprehensively verified the ability of multi-platform from orientation to positioning in the grouping of fusion algorithm, and proved the correctness and effectiveness of the fusion algorithm, which could greatly improve the accuracy of orientation and distance, and formed a highly credible simulation test evaluation ability. The system is extensible at the same time, and can also be applied to other simulation experiments.

P‐28: Dynamic Information Fusion System with Anti‐Diffraction Backplane Design for Mobile Application

In this research, we have developed dynamic information fusion system with an anti‐diffraction backplane design and applicate in the mobile field. On the backplane design, the results indicate the panel with intensity of the diffracted light about 0.2% compared with traditional pixel design by anti‐diffraction design. The transparent micro LED display with anti‐diffraction pixel design also shows clearer background image than traditional transparent display. The dynamic information fusion system integral three main technologies: gaze tracking, object identification, and display coordinate calculation for virtual image mapping. By integrating those technologies to achieve an AR‐based interactive system on anti‐diffraction direct view transparent display. Such a system can provide passengers with clear interactive information of virtual and real integration in the mobile field.

High-Precision Positioning Method of Coal Shearer in the Underground Environment Based on Rail Kinematics Model

The high-precision positioning of the shearer is the key technology to realize the automation of longwall mining. Since mine is a Global Position System (GPS)-denied environment, highly autonomous Inertial Navigation System (INS)/odometer integrated navigation has been widely used. At present, the shearer positioning method based on INS/odometer has been challenging to meet the requirements of long-time and high-precision mining. Aiming at the high-precision navigation in the complex mining environment, this paper constructs a comprehensive rail kinematics model of the shearer that does not rely on external sensors. By analyzing the kinematic characteristics of the shearer and the scraper conveyor during the longwall mining process, a method of information fusion and navigation system fault diagnosis based on the assistance of the shearer rail kinematics model was proposed. According to the working principle of the shearer rails and hydraulic supports, the characteristics of the trajectory deviation caused by the sensor fault of the hydraulic support are analyzed. Combined with the engineering requirements of shearer mining, the model fault identification was carried out by the fading probability ratio detection algorithm. The simulation results show that the proposed algorithm effectively improves the positioning of shearer accuracy in multiple cutting cycles. At the same time, it avoids the influence of the rail deviation caused by the rail kinematics model fault on the positioning of the shearer.