Multi-sensor Data Fusion Method Research Articles

Evidence combination based on prospect theory for multi-sensor data fusion

Multi-sensor data fusion (MSDF) is an efficient technology to enhance the performance of the system with the involvement of different kinds of sensors, which are broadly utilized in many fields at present. However, the data obtained from multi-sensors may have different degrees of uncertainty in the practical applications. Evidence theory is very useful to convey and manage uncertainty without a priori probability, so that it has been proverbially adopted in the information fusion fields. However, in the face of conflicting evidences, it has the possibility of producing counterintuitive results via conducting the Dempster’s combination rule (DCR). To solve the above-mentioned issue, a hybrid MSDF method is exploited through integrating a newly defined evidential credibility measure of evidences based on prospect theory and the evidence theory. More specifically, a series of concepts for the evidential credibility measure are first presented, including the local credibility degree, global credibility degree, evidential credibility estimation and credibility prospect value function to comprehensively describe the award and punish grades in terms of credible evidence and incredible evidence, respectively. Based on the above researches, an appropriate weight for each evidence can be obtained. Ultimately, the weight of each evidence is leveraged to amend the primitive evidences before conducting DCR. The results attained in the experiments demonstrate that the hybrid MSDF approach is efficient and superior to handle conflict evidences as well as the application in data fusion problems.

Multi-sensor data fusion for accurate surface modeling

Multi-sensor data fusion is advantageous while fusing data from heterogeneous range sensors, for scanning a scene containing both fine and coarse details. This paper presents a new multi-sensor range data fusion method with the aim to increase the descriptive contents of the entire generated surface model. First, a new training framework of the scanned range dataset to solve the relaxed Gaussian mixture model-based method by applying the convex relaxation technique is presented. The classification of the range data is based on a trained statistical model. In the data fusion experiments, a laser range sensor and Kinect (V1) are used. Based on the segmentation criterion, the range data fusion is performed by integration of the finer regions range data obtained from a laser range sensor with the coarser regions of the Kinect range data. The fused range information overcomes the weaknesses of the respective range sensors, i.e., the laser scanner is accurate but takes time while the Kinect is fast but not very accurate. The surface model of the fused range dataset generates a highly accurate, realistic surface model of the scene. The experimental results demonstrate robustness of the proposed approach.

A Multisensor Data Fusion Method Based on Gaussian Process Model for Precision Measurement of Complex Surfaces.

As multisensor measurement technology is rapidly applied in industrial production, one key issue is the data fusion procedure by combining several datasets from multiple sensors to obtain the overall geometric measurement. In this paper, a multisensor data fusion method based on a Gaussian process model is proposed for complex surface measurements. A robust surface registration method based on the adaptive distance function is firstly used to unify the coordinate systems of different measurement datasets. By introducing an adjustment model, the residuals between several independent datasets from different sensors are then approximated to construct a Gaussian process model-based data fusion system. The proposed method is verified through both simulation verification and actual experiments, indicating that the proposed method can fuse multisensor measurement datasets with better fusion accuracy and faster computational efficiency compared to the existing method.

A Multi-sensor Data Fusion Method for Nondestructive Testing of Oil Pipelines

A Multi-sensor Data Fusion Method for Nondestructive Testing of Oil Pipelines

Calculating potential evapotranspiration and single crop coefficient based on energy balance equation using Landsat 8 and Sentinel-2

Evapotranspiration is considered to be an important component of allocating water to agricultural sector; therefore, the more accurate this parameter is, the more optimized the water use can be. This study was conducted in order to evaluate the Landsat 8 and Sentinel-2 data (A and B), both separately and combined, in potential evapotranspiration (ETp) and single crop coefficient (Kc) estimations. Field measurements such as crop height, leaf area index (LAI), land surface temperature (LST), air temperature above canopy (AT), and spectral data were exploited in the evaluating process throughout the entirety of 2017–18 growing season under winter wheat and barley cultivations in the Agricultural Research Farms of the University of Tehran. The novel method of Multi-Sensor Data Fusion using the Priestly-Taylor equation was taken into practice for satellite-based ETp (MSDF-ET) calculation from the combination of MODIS thermal and Landsat 8 and Sentinel-2 multispectral data. Thermal images were downscaled by the means of the TsHARP algorithm. Thus, prior to ETp calculation, the thermal sharpening algorithm calculated using different spectral indices (SI) was assessed. The SI included NDVI, SAVI, SR, NDWI, NDWIg, and LSWI. The subsequent results were representative of the LSWI qualification under both Landsat 8 and Sentinel-2 conditions against thermal and spectral measurements. Also the satellite-based ETp strongly correlated with the ETp derived from the field data illuminating the promising accuracy of the MSDF-ET method in both Landsat 8 and Sentinel-2 data. In the end, the time series of Kc obtained from the combination of satellites were fairly indicative of the real-world variations under different vegetation cover and crop growth stages. Overall, using Landsat 8 and Sentinel-2 products in integration with each other could significantly result in more reliable decisions in agricultural water resources management.

An Improved Multisensor Data Fusion Method and Its Application in Fault Diagnosis

A multisensor system is used to improve the ability of monitoring and tracking accuracy of an engineering system since only one sensor is not enough, whereupon, faulty diagnosis, as a typical information fusion problem which is under multisensor environment, has attracted much attention in recent years. The evidence theory has been widely used to solve this problem. However, when there is a high level of conflict between the information gathered from different sensors, the counter-intuitive result could be obtained when using the classical Dempster’s combination rule. To address this problem, an improved multisensor data fusion method is proposed to fuse the data collected from multisensors. Some numerical examples are illustrated to show that the proposed method is effective and feasible. Moreover, the fusion results using different methods are analyzed, which indicate the superiority and stronger application of the proposed method in the field of fault diagnosis.

A Novel Fuzzy Approach for Combining Uncertain Conflict Evidences in the Dempster-Shafer Theory

Fusing conflict evidences is one of the fundamental needs to data fusion, but this task is challenging in the decision-making domain because of the fusion of ever-increasing uncertain data. In this paper, a novel fuzzy-based multi-sensor data fusion method is proposed for fusing high-conflict uncertain data and avoiding the counter-intuition problem. Our key idea is to introduce the fuzzy inference mechanism into the similarity measurement model to measure conflict degree between the evidences. On this basis, belief entropy is used to calculate the uncertainty of evidences, so as to express the relative importance of the evidences. The reliability of evidences can be obtained by the credibility which is gained through the above method, and the quantitative information volume is used to revise each credibility degree to get the final weight according to the evidence. The numerical experimental results demonstrate that the presented method is feasible and effective in dealing with conflicting evidences. In addition, the application of fault diagnosis is given to show that the proposed approach is effective and advantageous compared with state-of-the-art approaches.

Consensus Multisensor Data Fusion Algorithm Based on Dynamic Hierarchical Clustering Analysis

To improve the precision of consensus multi-sensor data fusion method, the reliability of sensor data must be considered. The shortcomings of the existing consensus multi-sensor data fusion algorithms, such as asymmetry of distance matrix, and the subjectivity of the threshold determination of connection matrix were discussed, and a new confidence distance was defined. A new consensus multi-sensor data fusion algorithm based on dynamic hierarchical clustering was proposed. The results of simulation experiments show that this method is better than the existing methods.

Multi-sensor data fusion based on the belief divergence measure of evidences and the belief entropy

Multi-sensor data fusion technology plays an important role in real applications. Because of the flexibility and effectiveness in modeling and processing the uncertain information regardless of prior probabilities, Dempster–Shafer evidence theory is widely applied in a variety of fields of information fusion. However, counter-intuitive results may come out when fusing the highly conflicting evidences. In order to deal with this problem, a novel method for multi-sensor data fusion based on a new belief divergence measure of evidences and the belief entropy was proposed. First, a new Belief Jensen–Shannon divergence is devised to measure the discrepancy and conflict degree between the evidences; then, the credibility degree can be obtained to represent the reliability of the evidences. Next, considering the uncertainties of the evidences, the information volume of the evidences are measured by making use of the belief entropy to indicate the relative importance of the evidences. Afterwards, the credibility degree of each evidence is modified by taking advantage of the quantitative information volume which will be utilized to obtain an appropriate weight in terms of each evidence. Ultimately, the final weights of the evidences are applied to adjust the bodies of the evidences before using the Dempster’s combination rule. A numerical example is illustrated that the proposed method is feasible and effective in handling the conflicting evidences, where the belief value of target increases to 99.05%. Furthermore, an application in fault diagnosis is given to demonstrate the validity of the proposed method. The results show that the proposed method outperforms other related methods where the basic belief assignment (BBA) of the true target is 89.73%.

Field-scale mapping of evaporative stress indicators of crop yield: An application over Mead, NE, USA

The Evaporative Stress Index (ESI) quantifies temporal anomalies in a normalized evapotranspiration (ET) metric describing the ratio of actual-to-reference ET (fRET) as derived from satellite remote sensing. At regional scales (3–10 km pixel resolution), the ESI has demonstrated the capacity to capture developing crop stress and impacts on regional yield variability in water-limited agricultural regions. However, its performance in some regions where the vegetation cycle is intensively managed appears to be degraded due to spatial and temporal limitations in the standard ESI products. In this study, we investigated potential improvements to ESI by generating maps of ET, fRET, and fRET anomalies at high spatiotemporal resolution (30-m pixels, daily time steps) using a multi-sensor data fusion method, enabling separation of landcover types with different phenologies and resilience to drought. The study was conducted for the period 2010–2014 covering a region around Mead, Nebraska that includes both rainfed and irrigated crops. Correlations between ESI and measurements of maize yield were investigated at both the field and county level to assess the potential of ESI as a yield forecasting tool. To examine the role of crop phenology in yield-ESI correlations, annual input fRET time series were aligned by both calendar day and by biophysically relevant dates (e.g. days since planting or emergence). At the resolution of the operational U.S. ESI product (4 km), adjusting fRET alignment to a regionally reported emergence date prior to anomaly computation improves r2 correlations with county-level yield estimates from 0.28 to 0.80. At 30-m resolution, where pure maize pixels can be isolated from other crops and landcover types, county-level yield correlations improved from 0.47 to 0.93 when aligning fRET by emergence date rather than calendar date. Peak correlations occurred 68 days after emergence, corresponding to the silking stage for maize when grain development is particularly sensitive to soil moisture deficiencies. The results of this study demonstrate the utility of remotely sensed ET in conveying spatially and temporally explicit water stress information to yield prediction and crop simulation models.

RETRACTED ARTICLE: Multimedia and multi-feature cluster fusion model based on saliency for mobile network applications

This paper introduces the concept, advantages, structure, method and application of multisensor integration and data fusion, and lists four different integration characteristics of sensors. Data fusion technology combines data from different sensors or other information sources in order to improve the accuracy of location and feature estimation. In the process of data fusion, modeling includes signal model, noise model, converter model, data transformation model and fusion model. The data fusion model includes the fusion method and the structure. This paper introduces the integrated, distributed and hybrid fusion structures, and compares them. The visual saliency map to image processing technology depends on the quality of the obtained good results, the existing visual saliency detection method is usually only detected by visual saliency map attribute rough, seriously affected the image processing results. Therefore, a visual saliency detection method based on Bayesian theory and statistical learning is proposed to detect the visual saliency of the image. The method is based on Bayesian theory of the significance of static images. According to the bottom-up visual saliency model, the ROC curve was used for quantitative evaluation in the two standard data sets. The results show that the nonlinear combination effect is better than the linear combination.

Multi-sensor image fusion based on regional characteristics

Multi-sensor data fusion method has been widely investigated in recent years. This article presents a novel fusion algorithm based on regional characteristics for combining infrared and visible light images in order to achieve an image with clear objects and high-resolution scene. First, infrared objects are extracted by region growing and guided filter. Second, the whole scene is divided into the objects region, the smooth region, and the texture region according to different regional characteristics. Third, the non-subsampled contourlet transform is used on infrared and visible images. Then, different fusion rules are applied to different regions, respectively. Finally, the fused image is constructed by the inverse non-subsampled contourlet transform with all coefficients. Experimental results demonstrate that the proposed objects extraction algorithm and the fusion algorithm have good performance in objective and subjective assessments.

A Multi-Sensor Data Fusion Approach for Atrial Hypertrophy Disease Diagnosis Based on Characterized Support Vector Hyperspheres.

Disease diagnosis can be performed based on fusing the data acquired by multiple medical sensors from patients, and it is a crucial task in sensor-based e-healthcare systems. However, it is a challenging problem that there are few effective diagnosis methods based on sensor data fusion for atrial hypertrophy disease. In this article, we propose a novel multi-sensor data fusion method for atrial hypertrophy diagnosis, namely, characterized support vector hyperspheres (CSVH). Instead of constructing a hyperplane, as a traditional support vector machine does, the proposed method generates “hyperspheres” to collect the discriminative medical information, since a hypersphere is more powerful for data description than a hyperplane. In detail, CSVH constructs two characterized hyperspheres for the classes of patient and healthy subject, respectively. The hypersphere for the patient class is developed in a weighted version so as to take the diversity of patient instances into consideration. The hypersphere for the class of healthy people keeps furthest away from the patient class in order to achieve maximum separation from the patient class. A query is labelled by membership functions defined based on the two hyperspheres. If the query is rejected by the two classes, the angle information of the query to outliers and overlapping-region data is investigated to provide the final decision. The experimental results illustrate that the proposed method achieves the highest diagnosis accuracy among the state-of-the-art methods.

A Weighted Belief Entropy-Based Uncertainty Measure for Multi-Sensor Data Fusion.

In real applications, how to measure the uncertain degree of sensor reports before applying sensor data fusion is a big challenge. In this paper, in the frame of Dempster–Shafer evidence theory, a weighted belief entropy based on Deng entropy is proposed to quantify the uncertainty of uncertain information. The weight of the proposed belief entropy is based on the relative scale of a proposition with regard to the frame of discernment (FOD). Compared with some other uncertainty measures in Dempster–Shafer framework, the new measure focuses on the uncertain information represented by not only the mass function, but also the scale of the FOD, which means less information loss in information processing. After that, a new multi-sensor data fusion approach based on the weighted belief entropy is proposed. The rationality and superiority of the new multi-sensor data fusion method is verified according to an experiment on artificial data and an application on fault diagnosis of a motor rotor.

An Adaptive Multi-Sensor Data Fusion Method Based on Deep Convolutional Neural Networks for Fault Diagnosis of Planetary Gearbox.

A fault diagnosis approach based on multi-sensor data fusion is a promising tool to deal with complicated damage detection problems of mechanical systems. Nevertheless, this approach suffers from two challenges, which are (1) the feature extraction from various types of sensory data and (2) the selection of a suitable fusion level. It is usually difficult to choose an optimal feature or fusion level for a specific fault diagnosis task, and extensive domain expertise and human labor are also highly required during these selections. To address these two challenges, we propose an adaptive multi-sensor data fusion method based on deep convolutional neural networks (DCNN) for fault diagnosis. The proposed method can learn features from raw data and optimize a combination of different fusion levels adaptively to satisfy the requirements of any fault diagnosis task. The proposed method is tested through a planetary gearbox test rig. Handcraft features, manual-selected fusion levels, single sensory data, and two traditional intelligent models, back-propagation neural networks (BPNN) and a support vector machine (SVM), are used as comparisons in the experiment. The results demonstrate that the proposed method is able to detect the conditions of the planetary gearbox effectively with the best diagnosis accuracy among all comparative methods in the experiment.

Impact of Tile Drainage on Evapotranspiration in South Dakota, USA, Based on High Spatiotemporal Resolution Evapotranspiration Time Series From a Multisatellite Data Fusion System

Soil drainage is a widely used agricultural practice in the midwest USA to remove excess soil water to potentially improve the crop yield. Research shows an increasing trend in baseflow and streamflow in the midwest over the last 60 years, which may be related to artificial drainage. Subsurface drainage (i.e., tile) in particular may have strongly contributed to the increase in these flows, because of its extensive use and recent gain in the popularity as a yield-enhancement practice. However, how evapotranspiration (ET) is impacted by tile drainage on a regional level is not well-documented. To explore spatial and temporal ET patterns and their relationship to tile drainage, we applied an energy balance-based multisensor data fusion method to estimate daily 30-m ET over an intensively tile-drained area in South Dakota, USA, from 2005 to 2013. Results suggest that tile drainage slightly decreases the annual cumulative ET, particularly during the early growing season. However, higher mid-season crop water use suppresses the extent of the decrease of the annual cumulative ET that might be anticipated from widespread drainage. The regional water balance analysis during the growing season demonstrates good closure, with the average residual from 2005 to 2012 as low as -3 mm. As an independent check of the simulated ET at the regional scale, the water balance analysis lends additional confidence to the study. The results of this study improve our understanding of the influence of agricultural drainage practices on regional ET, and can affect future decision making regarding tile drainage systems.

A weighted least square based data fusion method for precision measurement of freeform surfaces

The trend towards product miniaturisation and multi-functionality constitutes a driving force for the application of complex surfaces in many fields such as advanced optics. The precision measurement of these surfaces should be carried out at multiple scales, of which process commonly involves several datasets obtained from different sensors. This paper presents a weighted least square based multi-sensor data fusion method for such measurement. The method starts from unifying the coordinate frames of the measured datasets using an intrinsic feature based surface registration method. B-spline surface is used to fit linear surface model to each identified overlapping area of the registered datasets, respectively. By forming a common basis function, the fitted surface models and the corresponding residuals are then combined to construct a weighted least square based data fusion system which is used to generate a fused surface model. An analysis of the uncertainty propagation in data fusion process is also given. Both computer simulation and actual measurement on various freeform surfaces are conducted to verify the validity of proposed method. The results indicate that the proposed method is capable of fusing multi-sensor measured datasets with notable reduction of the measurement uncertainty.

New Method of Multi-sensor Data Fusion Based on Multiscale Analysis and UKF

New Method of Multi-sensor Data Fusion Based on Multiscale Analysis and UKF

Multi-sensor data fusion methods for indoor localization under collinear ambiguity

Sensor node localization in mobile ad-hoc sensor networks is a challenging problem. Often, the anchor nodes tend to line up in a linear fashion in a mobile sensor network when nodes are deployed in an ad-hoc manner. This paper discusses novel node localization methods under the conditions of collinear ambiguity of the anchors. Additionally, the work presented herein also describes a methodology to fuse data available from multiple sensors for improved localization performance under conditions of collinear ambiguity. In this context, data is first acquired from multiple sensors sensing different modalities. The data acquired from each sensor is used to compute attenuation models for each sensor. Subsequently, a combined multi-sensor attenuation model is developed. The fusion methodology uses a joint error optimization approach on the multi-sensor data. The distance between each sensor node and anchor is itself computed using the differential power principle. These distances are used in the localization of sensor nodes under the condition of collinear ambiguity of anchors. Localization error analysis is also carried out in indoor conditions and compared with the Cramer–Rao lower bound. Experimental results on node localization using simulations and real field deployments indicate reasonable improvements in terms of localization accuracy when compared to methods likes MLAR and MGLR.

Multi-Sensor Data Fusion Using a Relevance Vector Machine Based on an Ant Colony for Gearbox Fault Detection.

Sensors play an important role in the modern manufacturing and industrial processes. Their reliability is vital to ensure reliable and accurate information for condition based maintenance. For the gearbox, the critical machine component in the rotating machinery, the vibration signals collected by sensors are usually noisy. At the same time, the fault detection results based on the vibration signals from a single sensor may be unreliable and unstable. To solve this problem, this paper proposes an intelligent multi-sensor data fusion method using the relevance vector machine (RVM) based on an ant colony optimization algorithm (ACO-RVM) for gearboxes’ fault detection. RVM is a sparse probability model based on support vector machine (SVM). RVM not only has higher detection accuracy, but also better real-time accuracy compared with SVM. The ACO algorithm is used to determine kernel parameters of RVM. Moreover, the ensemble empirical mode decomposition (EEMD) is applied to preprocess the raw vibration signals to eliminate the influence caused by noise and other unrelated signals. The distance evaluation technique (DET) is employed to select dominant features as input of the ACO-RVM, so that the redundancy and inference in a large amount of features can be removed. Two gearboxes are used to demonstrate the performance of the proposed method. The experimental results show that the ACO-RVM has higher fault detection accuracy than the RVM with normal the cross-validation (CV).