Data Fusion Of Measurements Research Articles

Measurement data fusion with cascaded Kalman and complementary filter in the flight parameter indicator for hang-glider and paraglider

The research on an assistive instrument for hang-gliders and paragliders resulted in the three prototypes of the integrated device for sensing, processing and displaying data to the pilot. The data processing contains innovative data fusion and filtration algorithms with cascaded filter build with Kalman and complementary filters. The developed prototypes have been successfully tested in the laboratories and during flight tests.

A Novel Coupled State/Input/Parameter Identification Method for Linear Structural Systems

In many engineering applications, unknown states, inputs, and parameters exist in the structures. However, most methods require one or two of these variables to be known in order to identify the other(s). Recently, the authors have proposed a method called EGDF for coupled state/input/parameter identification for nonlinear system in state space. However, the EGDF method based solely on acceleration measurements is found to be unstable, which can cause the drift of the identified inputs and displacements. Although some regularization methods can be adopted for solving the problem, they are not suitable for joint input‐state identification in real time. In this paper, a strategy of data fusion of displacement and acceleration measurements is used to avoid the low‐frequency drift in the identified inputs and structural displacements for linear structural systems. Two numerical examples about a plane truss and a single‐stage isolation system are conducted to verify the effectiveness of the proposed modified EGDF algorithm.

Validation range for KF data fusion devices

Sensing devices are the main support for any experimental activity. The user expects that they are transparent, i.e., any measurement provides an assessment of a physical variable. Recent microelectronics developments caused significant modifications in the products offered by the market. Data fusion is the source of a recent jump in that technology, but the transparency of the result is no longer evident. In this paper, the authors consider the data fusion of displacement and acceleration measurements via a Kalman filter. The assemblage of two sensors is produced from scratch, and the critical aspects of the consequent data fusion are emphasized.

Stereo matching method for non-coding circular reference points based on motion consistency

Reference point-based fusion of multi-view measurement data is a popular method of object digitization that relies on the accurate matching of reference points in the images. However, due to the lack of coding information in the linear-structured light measurement, establishing a correspondence with the reference points becomes a challenging task. As the pose of a handheld linear-structured light measurement device undergoes very minor changes across neighboring views, this paper exploits this property and proposes a novel method of stereoscopic matching of non-coded reference circle points for a binocular linear-structured light measurement system. First, the polar line constraint is used to determine the initial set of matches, and these matches are used to compute the distance and direction of motion of the camera across neighboring views. The motion information is then used to eliminate the instances of mismatches from the initial results. Experiments on a self-locating linear-structured light measurement system show that the proposed algorithm can achieve an accurate and stable stereoscopic matching of binocular non-coded reference circle points.

Dynamic displacement estimation using data fusion

The paper describes a Kalman filtering technique for dynamic displacement estimation using accelerometer and laser sensor measurements. Data fusion of measurements from multiple sensors can give the more accurate results because of different advantages of sensors. Since the acceleration and displacement have different sampling rates, the multi-rate Kalman filter is applied. The filter is expanded with the fixed interval smoother to improve reconstruction accuracy of displacements. A modelled signal consisting of two sinus functions and Gaussian distributed noise is used to validate developed state-space model.

Real-time simultaneous identification of structural systems and unknown inputs without collocated acceleration measurements based on MEKF-UI

Identifications of excitations and structural systems are two crucial factors in structural health monitoring. There have been some research works focusing on simultaneous identification of structural systems and unknown inputs. Among them, the approach based on the extended Kalman filter with unknown inputs (EKF-UI) has the superiority of implementing identification with limited measurements and in real-time fashion. However, in previous EKF-UI approaches, it is necessary to have the measurements of acceleration responses at the locations where unknown inputs applied. In this paper, it is proposed to circumvent the above limitations for real time simultaneous identification of structural systems and unknown inputs without using collocated acceleration measurements. The proposed method is based on a novel modal extended Kalman filter with unknown inputs (MEFK-UI), which is the combination of extended Kalman filter in modal domain and the recent EKF-UI developed by the authors. Simultaneous identification of structural modal co-ordinates and unknown modal inputs are first conducted in the reduced dimension by modal truncation technique. Moreover, data fusion of displacement and acceleration measurements is used to prevent in real time the low-frequency drifts in the identification results. Subsequently, structural systems including structural parameters and unknown external inputs are identified in real time. Such an approach is not available in the literature. Several numerical examples are used to demonstrate the effectiveness of the proposed method.

Quantification of Coffea arabica and Coffea canephora var. robusta concentration in blends by means of synchronous fluorescence and UV-Vis spectroscopies

The potential of fluorescence, UV-Vis spectroscopies as well as the low- and mid-level data fusion of both spectroscopies for the quantification of concentrations of roasted Coffea arabica and Coffea canephora var. robusta in coffee blends was investigated. Principal component analysis was used to reduce data multidimensionality. To calculate the level of undeclared addition, multiple linear regression (PCA-MLR) models were used with lowest root mean square error of calibration (RMSEC) of 3.6% and root mean square error of cross-validation (RMSECV) of 7.9%. LDA analysis was applied to fluorescence intensities and UV spectra of Coffea arabica, canephora samples, and their mixtures in order to examine classification ability. The best performance of PCA-LDA analysis was observed for data fusion of UV and fluorescence intensity measurements at wavelength interval of 60nm. LDA showed that data fusion can achieve over 96% of correct classifications (sensitivity) in the test set and 100% of correct classifications in the training set, with low-level data fusion. The corresponding results for individual spectroscopies ranged from 90% (UV-Vis spectroscopy) to 77% (synchronous fluorescence) in the test set, and from 93% to 97% in the training set. The results demonstrate that fluorescence, UV, and visible spectroscopies complement each other, giving a complementary effect for the quantification of roasted Coffea arabica and Coffea canephora var. robusta concentration in blends.

Adaptive Wireless Biomedical Capsule Tracking Based on Magnetic Sensing

This paper studies wireless biomedical capsule (WBC) tracking, for magnetic sensing and actuation settings, where an embedded permanent magnet is used inside a passive WBC together with magnetic sensors outside the human body, producing a magnetic field around the WBC. First, a 2D WBC localization scheme is developed based on only magnetic sensing and adaptive recursive least squares (RLS) on-line parameter estimation with forgetting factor. Next, we propose a hybrid localization technique for simultaneous position and orientation estimation with high accuracy. The proposed hybrid localization technique is based on data fusion of magnetic measurements and electromagnetic signals emitted by the WBC for image transmission and other medical information using a similar adaptive RLS parameter estimation scheme. Later, the proposed localization techniques are integrated with an adaptive tracking law to construct an adaptive capsule tracking controller. The simulations demonstrate promising results for the efficiency and accuracy level of the proposed adaptive localization and tracking control schemes.

Data fusion of fluorescence and UV spectroscopies improves the detection of cocoa butter adulteration

The fraudulent addition of cheaper plant oils to cocoa butter is a real issue for the chocolate industry. The potential of fluorescence and UV and data fusion of these spectroscopies for the detection of cocoa butter adulteration with cocoa butter equivalents (CBEs) is investigated here. Principal component regression (PCR) models have been used to calculate the level of adulteration. A classification model has been built with the help of the principal component analysis (PCA) and the linear discriminant analysis (LDA). The LDA analysis has been applied to fluorescence intensities and UV spectra of cocoa butter, cocoa butter equivalents, and their mixtures in order to examine classification ability. The best prediction ability of adulteration was obtained for PCR data fusion models with the lowest errors of calibration (RMSEC) and validation (RMSEV) of 3.7 and 4.7%. The best performance of the PCA‐LDA analysis has been observed for data fusion of fluorescence and UV intensities measurements at wavelength intervals of 10 and 30 nm. The LDA shows that data fusion can achieve 95.2% correct classifications (sensitivity) in the validation data set, while the corresponding results for individual spectroscopies range from 66.7–90.5%. The results demonstrate that fluorescence and UV spectroscopies complement each other, giving a synergistic effect for the detection of cocoa butter adulteration with cocoa butter equivalents.Practical applications: The aim of this research is to develop a quick and cheap method of discriminating between cocoa butter and cocoa butter equivalents. The obtained results are satisfactory for classification of genuine cocoa butter and cocoa butter equivalents and for detection of cocoa butter adulterations with cheaper cocoa butter equivalents. The method is an important technical support of public health against profit‐driven practices.Application of fluorescence and UV spectroscopies to the detection of cocoa butter adulteration with cocoa butter equivalent and the synergy between both spectroscopies are investigated. The best prediction ability of MLR models i obtained for the data fusion model with the errors of calibration and validation of 3.7 and 4.7%. Compared to individual spectroscopies, data fusion shows better discrimination ability of PCA‐LDA model with the highest classification accuracy equal 100.0 and 95.2% in the calibration and validation data sets. The results show that fluorescence and UV spectroscopies give a synergistic effect for the detection of cocoa butter adulteration.

Augmented Cubature Kalman filter for nonlinear RTK/MIMU integrated navigation with non-additive noise

Abstract In order to enhance the capability of autonomous operation for small unmanned aerial vehicles (UAV), a MEMS-based inertial navigation system (INS)/global navigation satellite system (GNSS) integrated navigation method is proposed. An augmented Cubature Kalman filter is derived to fulfil the data fusion of precise GNSS real-time kinematic (RTK) solution and noisy inertial measurements. In the filter, Cubature Kalman filtering is adopted to handle the strong INS model nonlinearity caused by sudden and large UAV maneuvers, and the technique of state-augmentation is used to capture meaningful odd-order moment information and reduce the adverse impacts of non-additive noise in inertial measurements. It is analyzed that the basic difference between the augmented and non-augmented CKFs generally favors the augmented CKF, which is supported by a representative example and flight test. The results of flight test have also shown that the proposed augmented Cubature Kalman filtering method can complete more accurate navigation compared with the conventional EKF/UKF-based approaches.

Improved Kalman filter with unknown inputs based on data fusion of partial acceleration and displacement measurements

The classical Kalman filter (KF) provides a practical and efficient state estimation approach for structural identification and vibration control. However, the classical KF approach is applicable only when external inputs are assumed known. Over the years, some approaches based on Kalman filter with unknown inputs (KF-UI) have been presented. However, these approaches based solely on acceleration measurements are inherently unstable which leads poor tracking and so-called drifts in the estimated unknown inputs and structural displacement in the presence of measurement noises. Either on-line regularization schemes or post signal processing is required to treat the drifts in the identification results, which prohibits the real-time identification of joint structural state and unknown inputs. In this paper, it is aimed to extend the classical KF approach to circumvent the above limitation for real time joint estimation of structural states and the unknown inputs. Based on the scheme of the classical KF, analytical recursive solutions of an improved Kalman filter with unknown excitations (KF-UI) are derived and presented. Moreover, data fusion of partially measured displacement and acceleration responses is used to prevent in real time the so-called drifts in the estimated structural state vector and unknown external inputs. The effectiveness and performance of the proposed approach are demonstrated by some numerical examples.

Data fusion for accurate microscopic rough surface metrology

Data fusion for rough surface measurement and evaluation was analyzed on simulated datasets, one with higher density (HD) but lower accuracy and the other with lower density (LD) but higher accuracy. Experimental verifications were then performed on laser scanning microscopy (LSM) and atomic force microscopy (AFM) characterizations of surface areal roughness artifacts. The results demonstrated that the fusion based on Gaussian process models is effective and robust under different measurement biases and noise strengths. All the amplitude, height distribution, and spatial characteristics of the original sample structure can be precisely recovered, with better metrological performance than any individual measurements. As for the influencing factors, the HD noise has a relatively weaker effect as compared with the LD noise. Furthermore, to enable an accurate fusion, the ratio of LD sampling interval to surface autocorrelation length should be smaller than a critical threshold. In general, data fusion is capable of enhancing the nanometrology of rough surfaces by combining efficient LSM measurement and down-sampled fast AFM scan. The accuracy, resolution, spatial coverage and efficiency can all be significantly improved. It is thus expected to have potential applications in development of hybrid microscopy and in surface metrology.

Robust Hand Motion Tracking through Data Fusion of 5DT Data Glove and Nimble VR Kinect Camera Measurements.

Vision based interfaces for human computer interaction have gained increasing attention over the past decade. This study presents a data fusion approach of the Nimble VR vision based system, using the Kinect camera, with the contact based 5DT Data Glove. Data fusion was achieved through a Kalman filter. The Nimble VR and filter output were compared using measurements performed on (1) a wooden hand model placed in various static postures and orientations; and (2) three differently sized human hands during active finger flexions. Precision and accuracy of joint angle estimates as a function of hand posture and orientation were determined. Moreover, in light of possible self-occlusions of the fingers in the Kinect camera images, data completeness was assessed. Results showed that the integration of the Data Glove through the Kalman filter provided for the proximal interphalangeal (PIP) joints of the fingers a substantial improvement of 79% in precision, from 2.2 deg to 0.9 deg. Moreover, a moderate improvement of 31% in accuracy (being the mean angular deviation from the true joint angle) was established, from 24 deg to 17 deg. The metacarpophalangeal (MCP) joint was relatively unaffected by the Kalman filter. Moreover, the Data Glove increased data completeness, thus providing a substantial advantage over the sole use of the Nimble VR system.

The Indoor Localization and Tracking Estimation Method of Mobile Targets in Three-Dimensional Wireless Sensor Networks

Indoor localization is a significant research area in wireless sensor networks (WSNs). Generally, the nodes of WSNs are deployed in the same plane, i.e., the floor, as the target to be positioned, which causes the sensing signal to be influenced or even blocked by unpredictable obstacles, like furniture. However, a 3D system, like Cricket, can reduce the negative impact of obstacles to the maximum extent and guarantee the sensing signal transmission by using the line of sight (LOS). However, most of the traditional localization methods are not available for the new deployment mode. In this paper, we propose the self-localization of beacons method based on the Cayley–Menger determinant, which can determine the positions of beacons stuck in the ceiling; and differential sensitivity analysis (DSA) is also applied to eliminate measurement errors in measurement data fusion. Then, the calibration of beacons scheme is proposed to further refine the locations of beacons by the mobile robot. According to the robot’s motion model based on dead reckoning, which is the process of determining one’s current position, we employ the filter and the strong tracking filter (STF) to calibrate the rough locations, respectively. Lastly, the optimal node selection scheme based on geometric dilution precision (GDOP) is presented here, which is able to pick the group of beacons with the minimum GDOP from all of the beacons. Then, we propose the GDOP-based weighting estimation method (GWEM) to associate redundant information with the position of the target. To verify the proposed methods in the paper, we design and conduct a simulation and an experiment in an indoor setting. Compared to EKF and the filter, the adopted STF method can more effectively calibrate the locations of beacons; GWEM can provide centimeter-level precision in 3D environments by using the combination of beacons that minimizes GDOP.

Developing a Hybrid Intrusion Detection System Using Data Mining for Power Systems

Synchrophasor systems provide an immense volume of data for wide area monitoring and control of power systems to meet the increasing demand of reliable energy. The construction of traditional intrusion detection systems (IDSs) that use manually created rules based upon expert knowledge is knowledge-intensive and is not suitable in the context of this big data problem. This paper presents a systematic and automated approach to build a hybrid IDS that learns temporal state-based specifications for power system scenarios including disturbances, normal control operations, and cyber-attacks. A data mining technique called common path mining is used to automatically and accurately learn patterns for scenarios from a fusion of synchrophasor measurement data, and power system audit logs. As a proof of concept, an IDS prototype was implemented and validated. The IDS prototype accurately classifies disturbances, normal control operations, and cyber-attacks for the distance protection scheme for a two-line three-bus power transmission system.

Data Fusion Using Fuzzy Logic Techniques Supported by Modified Weighting Factors (FLMW)

This article describes a data fusion algorithm that uses fuzzy logic techniques. The algorithm involves weighting factors, the values of which change depending on whether the conditions dictated by processes of fuzzy logic have been met. By modifying the values of weighting factors, one can achieve a measurement signal with expected properties. The article includes a general description of the algorithm and an example of its application. The algorithm was developed for the purposes of data fusion for contactless measurement of the linear and angular position of an automatic laparoscopic device or a laparoscopic camera (hereinafter Laparoscope sleeve or LS). These measurements are used in the article to close the feedback loop on the position within a servomechanism with two degrees of freedom.

Design and implementation of a multi-sensor system for gas–water flow measurement

Gas–water two-phase flow is commonly encountered in industrial processes. It is still a challenging problem to accurately measure the flow parameters due to complex flow mechanics of the two-phase flow. This paper presents the design and implementation of a multi-sensor system for accurate measurement of gas–water flow in a horizontal pipe. Information detected from spatially distributed sensors can be analysed and integrated to extract effective information and obtain complete description of the gas–water flow, which is more reliable than that is estimated with a single sensor. The multi-sensor system included a dual-plane electrical resistance tomography sensor, a six-electrode conductance sensor, a dual-cone flowmeter, a pressure gauge and a temperature sensor. The position and distribution of the multiple sensors was investigated according to their measurement properties and the multi-sensor system was designed based on the combination of PXI modular instrument and FPGA technique. Experiments were conducted to study the performance of the multi-sensor system. Experimental results indicated that the proposed multi-sensor system could be used to investigate characteristics of the gas–water two-phase flow. This study underpins further study for the multi-sensor data fusion of two-phase flow measurement.

Displacement estimation of bridge structures using data fusion of acceleration and strain measurement incorporating finite element model

Recently, an indirect displacement estimation method using data fusion of acceleration and strain (i.e., acceleration-strain-based method) has been developed. Though the method showed good performance on beam-like structures, it has inherent limitation in applying to more general types of bridges that may have complex shapes, because it uses assumed analytical (sinusoidal) mode shapes to map the measured strain into displacement. This paper proposes an improved displacement estimation method that can be applied to more general types of bridges by building the mapping using the finite element model of the structure rather than using the assumed sinusoidal mode shapes. The performance of the proposed method is evaluated by numerical simulations on a deck arch bridge model and a three-span truss bridge model whose mode shapes are difficult to express as analytical functions. The displacements are estimated by acceleration-based method, strain-based method, acceleration-strain-based method, and the improved method. Then the results are compared with the exact displacement. An experimental validation is also carried out on a prestressed concrete girder bridge. The proposed method is found to provide the best estimate for dynamic displacements in the comparison, showing good agreement with the measurements as well.

Indirect Model Based Estimation of Cutting Force and Tool Tip Vibrational Behavior in Milling Machines by Sensor Fusion

Real time prediction of cutting tool condition and machined surface finish have been attractive research objectives over the last decades. However, providing practical and reliable solutions is still a demanding task for milling machine tools. One of the most challenging literature goals is to obtain a robust estimation of the cutting forces through indirect sensor measurements since many process and tool related quantities are indirectly linked to cutting forces. Another challenging issue in machining process monitoring and control is prediction of surface finish and quality. As the vibration plays a major role in the surface generation, this can be done by accurate prediction of the vibrational displacements at the tool tip during machining operation. In this paper, a novel model based estimation of cutting force and tool tip acceleration is designed and tested based on data fusion of different sensors measurements. In this context, two sensors (piezoelectric accelerometer and eddy-current displacement both mounted inside the spindle structure) have been utilized to acquire the experimental signals over a wide range of frequencies.In order to predict the above mentioned quantities, an optimal state estimator based on Kalman Filter (KF) is used. The models have been obtained by system identification method based on experimental measurements performed on a machine tool. The model based estimator is fed by real data. The results show that the estimation of the impulse force and tool tip acceleration can be achieved accurately in low and high frequency ranges by assigning different weights to the measurement sensors.

Adaptive fuzzy neuro-observer applied to low cost INS/GPS

A combined MEMS Inertial Navigation System (INS) with GPS is used to provide position and velocity data of land vehicles. Data fusion of INS and GPS measurements are commonly achieved through a conventional Extended Kalman filter (EKF). Considering the required accurate model of system together with perfect knowledge of predefined error models, the performance of the EKF is decreased due to unmodeled nonlinearities and unknown bias uncertainties of MEMS inertial sensors. Universal knowledge based approximators comprising of neural networks and fuzzy logic methods are capable of approximating the nonlinearities and the uncertainties of practical systems. First, in this paper, a new fuzzy neural network (FNN) function approximator is used to model unknown nonlinear systems. Second, the process of design and real-time implementation of an adaptive fuzzy neuro-observer (AFNO) in integrated low-cost INS/GPS positioning systems is proposed. To assess the long time performance of the proposed AFNO method, wide range tests of a real INS/GPS with a car vehicle have been performed. The unbiased estimation results of the AFNO show the superiority of the proposed method compared with the classic EKF and the adaptive neuro-observer (ANO) including a pure artificial neural network (ANN) function approximator.