Accuracy Of Data Fusion Research Articles

Probability-Based LIDAR-Camera Calibration Considering Target Positions and Parameter Evaluation Using a Data Fusion Map.

The data fusion of a 3-D light detection and ranging (LIDAR) point cloud and a camera image during the creation of a 3-D map is important because it enables more efficient object classification by autonomous mobile robots and facilitates the construction of a fine 3-D model. The principle behind data fusion is the accurate estimation of the LIDAR-camera's external parameters through extrinsic calibration. Although several studies have proposed the use of multiple calibration targets or poses for precise extrinsic calibration, no study has clearly defined the relationship between the target positions and the data fusion accuracy. Here, we strictly investigated the effects of the deployment of calibration targets on data fusion and proposed the key factors to consider in the deployment of the targets in extrinsic calibration. Thereafter, we applied a probability method to perform a global and robust sampling of the camera external parameters. Subsequently, we proposed an evaluation method for the parameters, which utilizes the color ratio of the 3-D colored point cloud map. The derived probability density confirmed the good performance of the deployment method in estimating the camera external parameters. Additionally, the evaluation quantitatively confirmed the effectiveness of our deployments of the calibration targets in achieving high-accuracy data fusion compared with the results obtained using the previous methods.

Effective hybrid video denoising and blending framework for Internet of Remote Things (IoRT) environments

The Internet of Remote Things (IoRT) has emerged as a transformative paradigm, merging IoT capabilities with remote technologies. IoRT environments, featuring interconnected sensors and robots, face challenges like sensor noise and low-light conditions, compromising video stream quality. This paper proposes a Hybrid Video Denoising and Blending Framework to address IoRT video data shortcomings. Leveraging spatial and temporal domain denoising techniques, the framework effectively removes noise while preserving crucial details. The inclusion of advanced blending algorithms facilitates seamless fusion of data from multiple sources, enhancing decision-making in real-world scenarios. The framework adopts a dynamic weighted averaging approach and an optimal sensor selection mechanism to intelligently choose informative data sources, improving blended output quality. Extensive experiments with a diverse IoRT dataset showcase the framework's superiority over state-of-the-art techniques, offering significant enhancements in video quality, noise reduction, and data fusion accuracy. Applications like surveillance, autonomous remotes, and industrial automation can benefit from the framework's ability to provide clearer, more reliable visual information. In conclusion, this research introduces a pioneering approach to mitigate video noise and enhance data fusion in IoRT, showcasing promising results and paving the way for further research in the integration of Remotes and IoT.

A novel industrial big data fusion method based on Q-learning and cascade classifier

The traditional industrial big data fusion algorithm has low efficiency and difficulty in processing high-dimensional data, this paper proposes a Q-learningbased cascade classifier model for industrial big data fusion. By combining cascade classifier and softmax classifier, feature extraction and data attribute classification of source industrial big data are completed in this cluster. In order to improve the classification rate, an improved Q-learning algorithm is proposed, which makes the improved algorithm randomly select actions in the early stage, and dynamically change in the late stage in the random selection of actions and actions with the highest reward value. It effectively improves the defects of traditional Q-learning algorithm that it is easy to fall into the local optimal and has slow convergence speed. The experimental results show that compared with other advanced fusion algorithms, the proposed method can greatly reduce the network energy consumption and effectively improve the efficiency and accuracy of data fusion under the same data volume.

Novel calibration technique for hybrid structured-light three-dimensional measurement system

Hybrid structured-light measurement technique is used for form measurement of structured composite surfaces. A hybrid structured-light measurement system contains a phase measuring deflectometry (PMD) subsystem and a fringe projection profilometry (FPP) subsystem. Each subsystem measures specular surfaces and rough surfaces based on structured-light reflection and projection principle, respectively. Calibration’s accuracy extremely effects data stitching precision between the subsystems. A novel calibration technique is explored for the hybrid structured-light system to complete reliable measurement accuracy. Calibration algorithms are developed based on designed calibration targets. Information of the calibration procedure are discussed and presented. Effectiveness of the proposed calibration technique has been conducted and verified through experiments by measuring structured composite samples. Experimental results demonstrate that the proposed technique can significantly improve data fusion accuracy of a hybrid structured-light measurement system.

Multisensor and Multiscale Data Integration Method of TLS and GPR for Three-Dimensional Detailed Virtual Reconstruction.

Integrated TLS and GPR data can provide multisensor and multiscale spatial data for the comprehensive identification and analysis of surficial and subsurface information, but a reliable systematic methodology associated with data integration of TLS and GPR is still scarce. The aim of this research is to develop a methodology for the data integration of TLS and GPR for detailed, three-dimensional (3D) virtual reconstruction. GPR data and high-precision geographical coordinates at the centimeter level were simultaneously gathered using the GPR system and the Global Navigation Satellite System (GNSS) signal receiver. A time synchronization algorithm was proposed to combine each trace of the GPR data with its position information. In view of the improved propagation model of electromagnetic waves, the GPR data were transformed into dense point clouds in the geodetic coordinate system. Finally, the TLS-based and GPR-derived point clouds were merged into a single point cloud dataset using coordinate transformation. In addition, TLS and GPR (250 MHz and 500 MHz antenna) surveys were conducted in the Litang fault to assess the feasibility and overall accuracy of the proposed methodology. The 3D realistic surface and subsurface geometry of the fault scarp were displayed using the integration data of TLS and GPR. A total of 40 common points between the TLS-based and GPR-derived point clouds were implemented to assess the data fusion accuracy. The difference values in the x and y directions were relatively stable within 2 cm, while the difference values in the z direction had an abrupt fluctuation and the maximum values could be up to 5 cm. The standard deviations (STD) of the common points between the TLS-based and GPR-derived point clouds were 0.9 cm, 0.8 cm, and 2.9 cm. Based on the difference values and the STD in the x, y, and z directions, the field experimental results demonstrate that the GPR-derived point clouds exhibit good consistency with the TLS-based point clouds. Furthermore, this study offers a good future prospect for the integration method of TLS and GPR for comprehensive interpretation and analysis of the surficial and subsurface information in many fields, such as archaeology, urban infrastructure detection, geological investigation, and other fields.

Characterization of ash content in wheat flour using data fusion

Ash detection played an important role in wheat flour quality testing. In this work, we proposed a method for the ash content detection in wheat flour, which based on hyperspectral (HSI) method fused with terahertz (THz) technology. Standard Normal Variable Correction (SNV) and Multiple Scattering Correction (MSC) were used for the pretreatment of HSI and THz data. Moreover, feature wavelengths were extracted by the Successive Projections Algorithm (SPA) and Feature weighting algorithms (Relieff), respectively. Hierarchical Extreme Learning Machine (H-ELM) model were used for the analysis of two spectral data, which combined with data fusion strategy. The prediction coefficient of determination (r2) and the root mean square error of prediction (RMSEP) were employed to evaluate the performance of the model effectively. Results suggested that the model accuracy of data fusion was better than that of single spectrum model. Compared to other fused method, this model obtained better results at r2 = 0.989 and RMSEP = 0.015, which based on the SPA-selected HSI feature data fused with the Relieff-selected THz feature data. This study demonstrates that data fusion analysis offers a way to detect ash content in wheat flour, which based on HSI method and THz technology.

Data Fusion and Processing Technology of Wireless Sensor Network for Privacy Protection

Data fusion and privacy protection technologies are both the research focuses in the field of wireless sensor networks. When the sensor network is in a harsh environment, the sensor nodes will face the danger of malicious entity attack in the data fusion progress. The efficiency and privacy protection of sensor network data fusion are very important. The traditional data fusion privacy protection algorithm has the problems of low data fusion efficiency and low privacy protection level. These problems are to be solved in this study. An improved cluster-based privacy data aggregation (I-CPDA) is proposed, which combines data slicing and false interference data technology. The experimental results of the algorithm show that the data fusion accuracy of the I-CPDA algorithm increases faster than the traditional algorithm with the time interval increasing, and the highest value reaches 90.7%. The fusion accuracy of the traditional CPDA algorithm under the same environment is 68.7%. In the actual test, the interception success rate of the I-CPDA algorithm for data attacks reached 90.74%, while the traditional CPDA was only 76.66. In addition, when the number of nodes in the cluster is 15, the data traffic of the I-CPDA is 56, while the data traffic of the traditional CPDA algorithm in the same environment exceeds 200. Compared with the currently widely used traditional algorithms, the I-CPDA algorithm has obvious advantages in terms of fusion effect, privacy, and efficiency and can be put into practical application.

Remote-sensing image data fusion processing technology based on multi-level fuzzy judgment

Remote sensing image technology is of great significance for dynamic management and monitoring of ground buildings. In order to improve the data fusion ability of remote sensing image of ground buildings, a data fusion method of remote sensing image of ground buildings based on multi-level fuzzy evaluation is proposed. This method constructs a remote sensing image acquisition model of ground buildings, and uses image enhancement methods to realize the gray information analysis and image enhancement of the remote sensing image rate of ground buildings. Finally, combining the remote sensing image data fusion method and the fuzzy region reconstruction method, it reconstructs the pixels of the dynamically changed ground buildings. The simulation results show that the remote sensing image data fusion accuracy of ground buildings is good, and the remote sensing feature extraction accuracy of ground buildings is high. The dynamic real-time monitoring of remote sensing image of ground buildings is realized.

An online transfer learning model for wind turbine power prediction based on spatial feature construction and system-wide update

Accurate prediction of wind turbine power is important for the safe operation of wind farms. However, most of the previous online transfer learning methods are partially updated and time-consuming. Here we propose a novel system-wide update online transfer learning model to overcome these shortcomings. To improve the multi-source data fusion accuracy, a new time trend quantification method is applied to expand the data source, a convolutional neural network multi-source data fusion method is proposed to reduce the dimension of data, and a Hilbert spatial feature construction method is used to construct spatial information of data. To achieve system-wide update and rapid prediction, we have deleted the weight unit of traditional method and added two data buffers. The results show that: (1) the proposed multi-source data processing method has the smallest mapping errors, which mean absolute error for all wind turbines is less than 32.1; (2) the proposed online transfer learning model has the highest prediction accuracy, which is higher than 92.5%.

Hierarchical network security situation awareness data fusion method in cloud computing environment

In order to improve the accuracy of hierarchical network security situational awareness data fusion and shorten the fusion time, this paper proposes a hierarchical network security situational awareness data fusion method in cloud computing environment. The hierarchical model is established to obtain the hierarchical structure of data fusion. Hierarchical network security situational awareness data are collected and processed in parallel by cloud computing technology. According to the data collection results, the similarity between security events is calculated by using the clustering idea, and the similar security events are merged to achieve the purpose of removing redundant events. The hierarchical network security situational awareness data is fused by grey relational analysis. Finally, the simulation results show that the accuracy of data fusion of this method is high, up to 98%, and the fusion time is short, the longest is 13 s. Compared with the comparison method, this method has a better performance, indicating that this method is suitable for data fusion of hierarchical network security situation awareness.

Dynamic Allocation Method of Economic Information Integrated Data Based on Deep Learning Algorithm.

In view of the low efficiency of traditional data fusion algorithms in wireless sensor networks and the difficulty in processing high-dimensional data, a new algorithm CNNMDA, based on the deep learning model is proposed to realize data fusion. Firstly, the algorithm trains the constructed feature extraction model CNNM at the sink node; then each terminal node extracts the original data features through CNNM and finally sends the fused data to the sink node, so as to reduce the data transmission amount and prolong the network life. Simulation experiments show that compared with similar fusion algorithms, the CNNMDA can greatly reduce network energy consumption under the same data amount, and effectively improve the efficiency and accuracy of data fusion. In order to solve the problem that parameter synchronization takes too long in synchronous parallel, a dynamic training data allocation algorithm in multimachine synchronous parallel is proposed. Based on the computing efficiency of compute nodes, the amount of sample data to be processed by nodes will be dynamically adjusted after each iteration. This mechanism not only enables the model to be synchronized and parallel but also reduces the time of waiting for gradient updates. Finally, a comparative experiment is carried out on the Tianhe-2 supercomputer, and the experimental results show that the proposed optimization mechanism achieves the expected effect.

Low-Energy Data Fusion Privacy Protection Algorithm for Three-Dimensional Wireless Sensor Network

Wireless sensor networks are now widely used in a variety of fields. It has the advantages of being low cost, practical, and adaptable. Methods of data fusion can help to increase efficiency. However, in the process of information transmission, once some nodes are invaded, the privacy of data will be threatened. Therefore, the research on its data privacy protection technology is very important. Under the premise of ensuring data privacy, existing data fusion methods of this type complete data fusion by preventing other nodes from knowing the private data collected by sensor nodes. Research on the current hot issues of data fusion privacy protection improves the shortcomings of traditional data fusion privacy protection technology and proposes an improvement, innovation, and three-dimensional space-oriented wireless sensor network low-energy data fusion privacy algorithm LEC-CPDA. Through simulation experiments and theoretical analysis, it is concluded that the LEC-CPDA algorithm can significantly improve the protection of privacy and improve the accuracy of data fusion.

Data fusion based wireless temperature monitoring system applied to intelligent greenhouse

In intelligent greenhouses, wireless sensor networks with uneven temperature distribution and low collection efficiency may lead to poor monitoring effects in real time. To improve the performance of the temperature monitoring system in intelligent greenhouses, a real-time fusion strategy for a hierarchical wireless sensor network (WSN) is proposed. The designed WSN has three layers. In the bottom, sensors collect and preprocess the temperature data of the greenhouse by an improved unscented Kalman filter. In the middle layer, each cluster-head sensor, as a local fusion center, is used to fuse the data collected from the bottom sensors by a parallel inverse covariance intersection fusion algorithm. In the top, a global fusion center is utilized to fuse the temperature data from the middle layer to reflect the global temperature of the greenhouse by an improved extreme learning machine algorithm. The designed algorithm applied in each layer ensures the efficiency and accuracy of data fusion in real time. Simulation results show that the designed fusion strategy effectively improves the fusion accuracy and realizes the real-time fusion. The performance of the designed temperature monitoring system is greatly improved.

A multi-sensors weighted data fusion method based on measurement traversal correction

In the case of multi-sensors weighted data fusion with unknown prior knowledge, a weighted data fusion method based on measurement traversal correction is proposed. The fusion accuracy of multi-sensors data fusion is influenced by both the measurement data accuracy and the data fusion weights of sensors. The measurement data of sensors is corrected through analyzing the reliability of different time data measured by sensors. The fusion weights of multi-sensors data fusion are optimal through depply analyzing the influence of weight distribution on multi-sensors data fusion accuracy. Typical examples are used to validate the proposed fusion algorithm, and the result shows that the fusion result is satisfying, and the algorithm is theoretical and practical.

Cramer-Rao Lower Bound Analysis of Data Fusion for Fingerprinting Localization in Non-Line-of-Sight Environments

This paper proposes a novel framework for analyzing the localization accuracy of data fusion for fingerprinting approaches in non-line-of-sight (NLOS) environments. Using simulation data generated for two very different NLOS environments (a suburban area of $3.3km\times 3.3km$ in Santa Clara, California, and a mountainous area of $11.4km\times 11.4km$ in the Caspian region), we establish novel channel models for measurement differences of three data types (received signal strength indicator (RSS), time of arrival (TOA) and direction of arrival (DOA)) at $K$ neighboring nodes of an arbitrary node. The crucial point is that the modeling errors for each of the three data types are shown to be jointly Gaussian distributed. Based on these measurement difference models, Cramer-Rao Lower Bound (CRLB) is used as a benchmark to evaluate $K$ -nearest neighbor (KNN) and Weighted $K$ -Nearest Neighbor (WKNN). It is shown that the proposed CRLB analyses can be employed to evaluate fingerprinting systems with various designs (such as different data types and fusion options) and different configurations (such as densities of reference nodes and numbers of anchor nodes) in diverse NLOS environments(such as suburban and mountainous regions).

The Analysis of Electronic Circuit Fault Diagnosis Based on Neural Network Data Fusion Algorithm

Symmetries play very important roles in the dynamics of electrical systems. The relevant electronic circuits with fault diagnostics, including the optimized neural network algorithm model, are designed on the basis of symmetry principles. In order to improve the efficiency of the circuit pressure test, a circuit pressure function equivalent compression test method based on the parallel neural network algorithm is proposed. For the implementation stage of the circuit pressure test, the improved modified node algorithm (MNA) is used to build an optimization model, and the circuit network is converted into an ordinary differential equation for the circuit pressure function equivalent compression test. The test aims to minimize flux. Then, backpropagation (BP) neural network algorithm data fusion is introduced to optimize the minimum flux model of the cyclic pressure functional equivalent compression test. Finally, a simulation experiment is carried out to verify the effectiveness of the algorithm in the accuracy and efficiency of the pressure test. The results show that the improved BP neural network improves the data fusion accuracy and shortens the sample training time; compared with the uncompressed algorithm, the running time of the proposed algorithm is greatly reduced and the execution efficiency is high; compared with the vascular pressure test method, there is no significant difference in the convergence accuracy and it is at a level of 10−5. Since the parallel computing problem is not considered in either of the two-pulse tube pressure test methods, the convergence time of the algorithm increases exponentially with the increase in the number of parallel threads. However, the algorithm in this research considers the problem of parallel execution and uses a quad-core processor, with no significant change in computing time and high computing efficiency. Therefore, BP neural network data fusion can be used for the fault diagnosis of electronic circuits, with a high operating efficiency and good development prospects.

Influence of different data fusion methods on the accuracy of three-dimensional displacements field

Combining multi-source data can improve the accuracy and the spatial resolution of the three-dimensional (3-D) displacements field. How to effectively integrate multi-source data to obtain high-precision and high spatial resolution 3-D displacements field is worthy of further study. The stochastic model and fusion model of integrating multi-source data affect the accuracy of data fusion. In this paper, based on the least squares method, the effects of different stochastic models and data fusion models on the 3-D displacements field’s accuracy are studied. The optimal method for estimating large-scale 3-D displacements field from integrated InSAR, leveling and GPS measurements is obtained. Then we realize the integrating InSAR, leveling and GPS measurements to obtain the high-precision 3-D displacements velocity field in Tianjin (China) from 2016 to 2018. The results are validated with GPS measurements at 6 independent stations, with the root mean squares (RMS) residuals of the discrepancies being 2.39 mm/yr, 2.54 mm/yr and 2.83 mm/yr in eastern, northern and vertical directions, respectively. By comparing different stochastic models, the 3-D displacements field obtained from multi-source data is optimized by the variance component estimation-least squares method, which is better than weighted least squares (WLS) method. By comparing different data fusion models, the accuracy of the horizontal displacements velocity is better than that of interpolated GPS results. The horizontal displacements component has a great influence on the vertical displacements velocity accuracy in the process of acquiring the 3-D displacements velocity by integrating InSAR, GPS and leveling measurements. This study provides a reference method for integrating multi-source data to obtain 3-D displacements field. This method effectively utilizes the advantages of GPS, InSAR and leveling measurements, and extends the limitations of single technical in describing surface-time scale applications. The 3-D displacements information with a large spatial scale and high spatial resolution provide a reliable data basis for studying the crustal movement and its dynamic mechanism.

Improving Spatial-Temporal Data Fusion by Choosing Optimal Input Image Pairs

Spatial and temporal data fusion approaches have been developed to fuse reflectance imagery from Landsat and the Moderate Resolution Imaging Spectroradiometer (MODIS), which have complementary spatial and temporal sampling characteristics. The approach relies on using Landsat and MODIS image pairs that are acquired on the same day to estimate Landsat-scale reflectance on other MODIS dates. Previous studies have revealed that the accuracy of data fusion results partially depends on the input image pair used. The selection of the optimal image pair to achieve better prediction of surface reflectance has not been fully evaluated. This paper assesses the impacts of Landsat-MODIS image pair selection on the accuracy of the predicted land surface reflectance using the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) over different landscapes. MODIS images from the Aqua and Terra platforms were paired with images from the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Landsat 8 Operational Land Imager (OLI) to make different pair image combinations. The accuracy of the predicted surface reflectance at 30 m resolution was evaluated using the observed Landsat data in terms of mean absolute difference, root mean square error and correlation coefficient. Results show that the MODIS pair images with smaller view zenith angles produce better predictions. As expected, the image pair closer to the prediction date during a short prediction period produce better prediction results. For prediction dates distant from the pair date, the predictability depends on the temporal and spatial variability of land cover type and phenology. The prediction accuracy for forests is higher than for crops in our study areas. The Normalized Difference Vegetation Index (NDVI) for crops is overestimated during the non-growing season when using an input image pair from the growing season, while NDVI is slightly underestimated during the growing season when using an image pair from the non-growing season. Two automatic pair selection strategies are evaluated. Results show that the strategy of selecting the MODIS pair date image that most highly correlates with the MODIS image on the prediction date produces more accurate predictions than the nearest date strategy. This study demonstrates that data fusion results can be improved if appropriate image pairs are used.

An Enhanced Linear Spatio-Temporal Fusion Method for Blending Landsat and MODIS Data to Synthesize Landsat-Like Imagery

Landsat and MODIS data have been widely utilized in many remote sensing applications, however, the trade-off between the spatial resolution and temporal frequency has limited their capacities in monitoring detailed spatio-temporal dynamics. Spatio-temporal fusion methods based on a linear model that considers the differences between fine- and coarse-spatial-resolution images as linear can effectively solve this trade-off problem, yet the existing linear fusion methods either regard the coefficients of the linear model as constants or have adopted regression methods to calculate the coefficients, both of which may introduce some errors in the fusion process. In this paper, we proposed an enhanced linear spatio-temporal fusion method (ELSTFM) to improve the data fusion accuracy. In the ELSTFM, it is not necessary to calculate the slope of the linear model, and the intercept, which can be deemed as the residual caused by systematic biases, is calculated based on spectral unmixing theory. Additionally, spectrally similar pixels in a given fine-spatial-resolution pixel’s neighborhood and their corresponding weights were used in the proposed method to mitigate block effects. Landsat-7/ETM+ and 8-day composite MODIS reflectance data covering two study sites with heterogeneous and homogenous landscapes were selected to validate the proposed method. Compared to three other typical spatio-temporal fusion methods visually and quantitatively, the predicted images obtained from ELSTFM could acquire better results for the two selected study sites. Furthermore, the resampling methods used to resample MODIS to the same spatial resolution of Landsat could slightly, but did not significantly influence the fusion accuracy, and the distributions of slopes of different bands for the two study sites could all be deemed as normal distributions with a mean value close to 1. The performance of ELSTFM depends on the accuracy of residual calculation at fine-resolution and large landscape changes may influence the fusion accuracy.

Collaborative fusion estimation over wireless sensor networks for monitoring CO2 concentration in a greenhouse

This paper investigates the data fusion problem over wireless sensor networks (WSNs) for monitoring the carbon dioxide (CO2) concentration in a greenhouse. CO2 concentration is an important environmental parameter in the greenhouse, and adequate regulation of CO2 concentration is certainly beneficial to the improvement of the crop growth efficiency in the greenhouse. Since the measurement of CO2 concentration is unavoidably subject to environmental interferences (e.g. noises), it is vitally important to estimate the true CO2 concentration through available sensor measurements over a WSN with given topology. In this paper, based on the Consensus–Kalman filter, the distributed estimation scheme is presented to improve the state estimation accuracy. After the distributed consensus estimation, the head node can conduct the fusion estimation on the data from the available sensor nodes. The packet loss phenomenon brought by unreliable communication links is reflected by the proportion of the faulty sensors. To further alleviate the effects from the packet losses, we propose a modified estimation scheme for the head node that combines the estimates from sensor node at both the previous and current time points, thereby enhancing the accuracy of data fusion. Simulation analysis is carried out on the collected information of CO2 concentration in the greenhouse in order to demonstrate the effectiveness of the proposed scheme.