Journal articles: 'Multisensor measurement'

1

Li, Song, Yongmei Cheng, Huibin Wang, and Shibo Gao. "Distributed Multisensor Multitarget Tracking Algorithm with Time-Offset Registration." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 38, no. 4 (2020): 797–805. http://dx.doi.org/10.1051/jnwpu/20203840797.

Full text

Abstract:

In multisensor systems, the signal processing delay, measurement acquisition delay, and other factors will lead to imprecisely time-stamped measurements, namely, the problem of time-offset. To deal with the measurement time offsets in distributed multisensor systems, a distributed multisensor multitarget tracking algorithm with time-offset registration is proposed. The local processors track multiple targets in the presence of false alarms and missed detections based on the joint probabilistic data association (JPDA) algorithm and the extended Kalman filter (EKF), providing the time-biased local tracks. In the global processor, in allusion to the global track accuracy degradation introduced by the time offsets of local tracks, the equivalent measurements are firstly constructed based on local tracks by using the inverse Kalman filter. The pseudo-measurement equation of time offset for constant velocity targets is derived and the pseudo-measurement calculation method is presented. Then, the pseudo-measurement based relative time-offset estimation algorithm is presented, by using the recursive least squares estimation (RLSE) and the Kalman filter (KF) to jointly estimate the state in space and time domains, respectively. Finally, a framework of distributed multisensor multitarget tracking with time-offset registration is presented, where the time-varying relative time-offset estimation and compensation, 'equivalent measurement to global track' association, and global track update are included. Simulations for multisensor multitarget tracking in the presence of false alarms and missed detections are conducted, demonstrating that the present algorithm effectively improves the accuracy of fused global tracks.

APA, Harvard, Vancouver, ISO, and other styles

2

Lin, Tsun-Kuo. "PCA/SVM-Based Method for Pattern Detection in a Multisensor System." Mathematical Problems in Engineering 2018 (2018): 1–11. http://dx.doi.org/10.1155/2018/6486345.

Full text

Abstract:

This paper presents a multivariate analysis framework for pattern detection in a multisensor system; the proposed principal component analysis (PCA)/support vector machine- (SVM-) based supervision scheme can identify patterns in the multisensory system. Although the PCA and SVM are commonly used in pattern recognition, an effective methodology using the PCA/SVM for multisensory system remains unexplored. Pattern detection in a multisensor system has long been a challenge. For example, object inspections in multisensor systems are difficult to perform because inspectors might fail to use multiple sensing devices when concurrently detecting different patterns. Therefore, to resolve this issue, this study proposes a novel framework for establishing indicators and corresponding thresholds to identify patterns in the system; it employs a feature-based scheme that integrates principal component analysis (PCA) with an SVM for effectively detecting patterns in the system. Experiments were conducted using a tactile and optical measurement system. The experimental results demonstrated that the proposed method can effectively identify patterns in multisensor systems by using a feature-based algorithm that combines PCA and SVM classification for detecting various patterns. Moreover, the proposed framework established alarm indicators and corresponding thresholds that can be used for pattern detection.

APA, Harvard, Vancouver, ISO, and other styles

3

Ding, Ji, Qiang Liu, Mingxuan Bai, and Pengpeng Sun. "A Multisensor Data Fusion Method Based on Gaussian Process Model for Precision Measurement of Complex Surfaces." Sensors 20, no. 1 (2020): 278. http://dx.doi.org/10.3390/s20010278.

Full text

Abstract:

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.

APA, Harvard, Vancouver, ISO, and other styles

4

Harmatys, Wiktor, Piotr Gąska, Adam Gąska, et al. "Applicability Assessment of Different Materials for Standards Ensuring Comparability of Optical and Tactile Coordinate Measurements." Materials 15, no. 12 (2022): 4128. http://dx.doi.org/10.3390/ma15124128.

Full text

Abstract:

Multisensor CMMs are systems with an established position on the market, but their popularity still grows, as they provide access to the advantages offered by tactile and contactless measurement methods. Yet there are still questions of the comparability of results obtained using the optical and tactile operation modes of multisensor system. This phenomenon can be assessed by measuring appropriate gauges, most often reference rings or spheres. Due to the completely different nature of probing processes for tactile and contactless measurements, the material from which reference object is made may significantly affect measurement results. In order to assess the influence of this factor on measurement accuracy, three reference spheres made from different materials were measured on optical multisensor CMMs. Measurements involved tactile measurements as well as optical measurements made using different probing systems: a video probe and white light sensor. Results obtained from performed experiments show large differences depending on the material used for spherical standard production. On the basis of obtained results, it can be stated that the best material for a reference object that can be used for comparability tests of tactile and optical measurements is a composite of alumina with at least one oxidic additive.

APA, Harvard, Vancouver, ISO, and other styles

5

Zhang, Jungen, and Shanglin Yang. "Bearings-only Tracking Based on Distributed Multisensor Pseudolinear Kalman Filter." International Journal of Circuits, Systems and Signal Processing 16 (March 28, 2022): 874–81. http://dx.doi.org/10.46300/9106.2022.16.107.

Full text

Abstract:

For bearings-only tracking (BOT), there are mainly two problems of nonlinear filtering and poor range observability. In the paper, a new distributed multisensor pseudolinear Kalman filter (PLKF) algorithm is proposed. The sensors use an instrumental vector PLKF (IV-PLKF) to process the measurements of the target independently, which can tackle the bias arising from the correlation between the measurement vector and pseudolinear noise by the bias compensation PLKF (BC-PLKF). The IV-PLKF embeds the recursive instrumental vector estimation method into the BC-PLKF, uses it to construct the instrumental vector, and applies the method of selective angle measurement to modify the local target state estimation and covariance. In the fusion center, the target state can be estimated by using the multisensor optimal information fusion criterion. Then the Cramer-Rao lower bound (CRLB) of multisensor BOT is derived. Simulation results show the effectiveness of the algorithm.

APA, Harvard, Vancouver, ISO, and other styles

6

Dou, Yin Feng, Wen Juan Qi, and Chen Jian Ran. "Weighted Measurement Fusion Kalman Filter for Multisensor Descriptor System." Applied Mechanics and Materials 373-375 (August 2013): 940–45. http://dx.doi.org/10.4028/www.scientific.net/amm.373-375.940.

Full text

Abstract:

For the multi-sensor descriptor system with correlated measurement noises and same measurement matrix, the reduced-order sub-systems are obtained, applying singular value decomposition method. And measurements of every sensor are transformed to the measurement of one state component. For this new reduced-order normal system, the new fused measurement can be obtained applying the weighted least squares method. Then, the weighted measurement fusion Kalman filter and its filtering error variance are presented, applying a single Kalman filter. This method avoids computing the cross-variances among all local filters, compared with the state fusion Kalman filtering algorithm. And the accuracy of this fused filter is higher than that of local filter and state fusion Kalman filter. A simulation example verifies its effectiveness.

APA, Harvard, Vancouver, ISO, and other styles

7

Lu, Xiaoke, Zhiguo Zhang, Qing Li, and Jinping Sun. "Robust Multisensor MeMBer Filter for Multiple Extended-Target Tracking." Mathematical Problems in Engineering 2021 (May 20, 2021): 1–11. http://dx.doi.org/10.1155/2021/9942365.

Full text

Abstract:

This paper develops a robust extended-target multisensor multitarget multi-Bernoulli (ET-MS-MeMBer) filter for enhancing the unsatisfactory quality of measurement partitions arising in the classical ET-MS-MeMBer filter due to increased clutter intensities. Specifically, the proposed method considers the influence of the clutter measurement set by introducing the ratio of the target likelihood to the clutter likelihood. With the constraint of the clutter measurement set, it can obtain better multisensor measurement partitioning results under the original two-step greedy partitioning mechanism. Subsequently, the single-target multisensor likelihood function for the clutter case is derived. Simulation results reveal a favorable comparison to the ET-MS-MeMBer filter in terms of accuracy in estimating the target cardinality and target state under conditions with increased clutter intensities.

APA, Harvard, Vancouver, ISO, and other styles

8

Cheremisina, Olga, Vladimir Kulagin, Suad El-Saleem, and Evgeny Nikulchev. "Multisensory Gas Analysis System Based on Reconstruction Attractors." Symmetry 12, no. 6 (2020): 964. http://dx.doi.org/10.3390/sym12060964.

Full text

Abstract:

The paper describes the substance image formation based on the measurements by multisensor systems and the possibility of the development of a gas analysis device like an electronic nose. Classification of gas sensors and the need for their application for the recognition of difficult images of multicomponent air environments are considered. The image is formed based on stochastic transformations, calculations of correlation, and fractal dimensions of reconstruction attractors. The paper shows images created for substances with various structures that were received with the help of a multisensor system under fixed measurement conditions.

APA, Harvard, Vancouver, ISO, and other styles

9

Pao, Lucy Y. "Measurement reconstruction approach for distributed multisensor fusion." Journal of Guidance, Control, and Dynamics 19, no. 4 (1996): 842–47. http://dx.doi.org/10.2514/3.21708.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Wei, Daozhi, Zhaoyu Zhang, Jiahao Xie, Liang fu Yao, and Ning Li. "Multisensor Hybrid Dynamic Alliance Formation Problem Using Sensitive Particle-Based Dynamic Discrete PSO." Mathematical Problems in Engineering 2021 (December 9, 2021): 1–14. http://dx.doi.org/10.1155/2021/2997983.

Full text

Abstract:

In recent years, with the wide application and popularization of artificial intelligence algorithm in the field of multisensor information processing, it has been a research hotspot to solve the problem of sensor alliance formation in the battlefield environment by using multisensor cross-cueing technology. Based on the establishment of the multisensor hybrid dynamic alliance model and objective function, a multisensor cross-cueing algorithm based on dynamic discrete particle swarm optimization (DDPSO) with sensitive particles is proposed and a mechanism of “predict re-predict” is proposed in the process of sensor handover. Simulations have verified the good convergence effect and small detection error of multisensor cross-cueing technology in solving alliance formation problems. Meanwhile, compared with “measurement and then update” and “predict and update” mechanisms, the proposed mechanism is more suitable to the changing combat environment. At the same time, to some extent, it also shows that the artificial intelligence algorithm is more suitable for multisensor information processing.

APA, Harvard, Vancouver, ISO, and other styles

11

Wang, Xin, Qi Dan Zhu, and Ye Bin Wu. "A Measurement Fusion Fault-Tolerating PID Control for Time-Delay System with Colored Noise Disturbance." Key Engineering Materials 419-420 (October 2009): 589–92. http://dx.doi.org/10.4028/www.scientific.net/kem.419-420.589.

Full text

Abstract:

The designing method of filtering, fault-tolerating, and fusing PID control is put forward, concerning multisensor time-delay system with colored noise disturbance. First of all, this method detects fault and isolate the data by the weighted square sum of residuals (WSSR) method which is measured by multisensors, then the data which is detected right will be measurement fused, and the fused data will be optimally filtered basing on modern time series analysis method. Finally, the global optimal estimation of measured data will be got, which will be brought back to the input endian in order to improve PID controlling accuracy. A 3-sensor servomotor control example shows the effectiveness of the method.

APA, Harvard, Vancouver, ISO, and other styles

12

Liu, Wen Qiang, Xue Mei Wang, and Zi Li Deng. "The Design of a Robust Weighted Measurement Fusion Steady-State Kalman Filter." Applied Mechanics and Materials 701-702 (December 2014): 624–29. http://dx.doi.org/10.4028/www.scientific.net/amm.701-702.624.

Full text

Abstract:

For the linear discrete-time multisensor time-invariant system with uncertain model parameters and measurement noise variances, by introducing fictitious noise to compensate the parameter uncertainties, using the minimax robust estimation principle, based on the worst-case conservative multisensor system with conservative upper bounds of measurement and fictitious noises variances, a robust weighted measurement fusion steady-state Kalman filter is presented. By the Lyapunov equation approach, it is proved that when the region of the parameter uncertainties is sufficient small, the corresponding actual fused filtering error variances are guaranteed to have a less-conservative upper bound. Simulation results show the effectiveness and correctness of the proposed results.

APA, Harvard, Vancouver, ISO, and other styles

13

Yuan Gao, Wen-Jing Jia, Xiao-Jun Sun, and Zi-Li Deng. "Self-Tuning Multisensor Weighted Measurement Fusion Kalman Filter." IEEE Transactions on Aerospace and Electronic Systems 45, no. 1 (2009): 179–91. http://dx.doi.org/10.1109/taes.2009.4805272.

Full text

APA, Harvard, Vancouver, ISO, and other styles

14

Zhou, Yu, Jianxin Qin, and Bingting Zha. "Multisensor-Based Maneuvering Target Tracking Algorithm under Non-Gaussian Measurement Noise." Journal of Physics: Conference Series 2478, no. 6 (2023): 062028. http://dx.doi.org/10.1088/1742-6596/2478/6/062028.

Full text

Abstract:

Abstract The UAVs have become a huge threat in the battlefield. The excellent maneuverability makes them competent for the actual combat operations such as investigation and strike. Therefore, accurate tracking of UAVs is critical. Centralized radar/IR fusion system has been widely concerned to track the UAVs because of the complementary characteristics between these two sensors. The appearance of non-Gaussian measurement noise produces a negative impact on tracking accuracy. This paper proposes a novel IMM-CMCSCKF algorithm which can improve the tracking accuracy of radar/IR fusion system for maneuvering target under non-Gaussian measurement noise. The heterogeneous measurements from radar and IR are firstly fused. Then, CMCSCKF algorithm is presented based on MCC to deal with non-Gaussian measurement noise. To improve maneuvering target tracking accuracy, CMCSCKF is embed into IMM algorithm. Numerical simulation verifies the effectiveness of the proposed IMM-CMCSCKF.

APA, Harvard, Vancouver, ISO, and other styles

15

Qi, Wen Juan, Peng Zhang, Zi Li Deng, and Yuan Gao. "Multisensor Covariance Intersection Fusion Kalman Filters." Applied Mechanics and Materials 373-375 (August 2013): 946–52. http://dx.doi.org/10.4028/www.scientific.net/amm.373-375.946.

Full text

Abstract:

For multisensor system with colored measurement noises, the common disturbance noises and measurement biases, the batch covariance intersection fusion (BCI) Kalman filter and the sequential covariance intersection fusion (SCI) Kalman filter are presented, which can avoid the computation of the local filtering errors and reduce the computational burden significantly. Under the linear unbiased minimum variance (ULMV) criterion, the three weighted fusion Kalman filters (weighted by matrices, scalars or diagonal matrices) are also presented. Their accuracy relations are analyzed and compared. Specially, the accuracy of the proposed covariance intersection fusion Kalman filters are higher than that of each local Kalman filters, and is lower than that of optimal fuser weighted by matrices. The geometric interpretation of the accuracy relations is given by the covariance ellipses. A Monte-Carlo simulation example for a tracking system verifies the correctness of the theoretical accuracy relations.

APA, Harvard, Vancouver, ISO, and other styles

16

Santhosh, K. V., Blessy Joy, and Swetha Rao. "Design of an Instrument for Liquid Level Measurement and Concentration Analysis Using Multisensor Data Fusion." Journal of Sensors 2020 (January 6, 2020): 1–13. http://dx.doi.org/10.1155/2020/4259509.

Full text

Abstract:

This paper presents the design of an instrument for measuring the liquid level. The objective of this proposed work is to measure the level of liquid accurately even with variations in liquid concentration. The designed instrument should also be able to compute the concentration of additives in the liquid. For this purpose, a multisensor model comprising a capacitive level sensor (CLS), ultrasonic level sensor (ULS), and capacitance pressure sensor is used to acquire information of the liquid. The data acquired from all these sensors are processed using Pau’s multisensor data fusion framework to compute the level of liquid along with the concentration of additives added to the solution. Pau’s framework consists of alignment, association function, analysis, and representation functions. The designed multisensor technique is tested with real-life data for varying liquid levels and additives. The results obtained show that the successful implementation of the proposed objective producing a root mean square of percentage error is 1.1% over the full scale is possible.

APA, Harvard, Vancouver, ISO, and other styles

17

Zhou, Gongjian, Shizhe Bu, and Thiagalingam Kirubarajan. "Simultaneous Spatiotemporal Bias Compensation and Data Fusion for Asynchronous Multisensor Systems." Chinese Journal of Information Fusion 1, no. 1 (2024): 16–32. http://dx.doi.org/10.62762/10.62762/cjif.2024.361881.

Full text

Abstract:

Bias estimation of sensors is an essential prerequisite for accurate data fusion. Neglect of temporal bias in general real systems prevents the existing algorithms from successful application. In this paper, both spatial and temporal biases in asynchronous multisensor systems are investigated and two novel methods for simultaneous spatiotemporal bias compensation and data fusion are presented. The general situation that the sensors sample at different times with different and varying periods is explored, and unknown time delays may exist between the time stamps and the true measurement times. Due to the time delays, the time stamp interval of the measurements from different sensors may be different from their true measurement interval, and the unknown difference between them is considered as the temporal bias and augmented into the state vector to be estimated. Multisensor measurements are collected in batch processing or sequential processing schemes to estimate the augmented state vector, results in two spatiotemporal bias compensation methods. In both processing schemes, the measurements are formulated as functions of both target states and spatiotemporal biases according to the time difference between the measurements and the states to be estimated. The Unscented Kalman Filter is used to handle the nonlinearity of the measurements and produce spatiotemporal bias and target state estimates simultaneously. The posterior Cramer-Rao lower bound (PCRLB) for spatiotemporal bias and state estimation is presented and simulations are conducted to demonstrate the effectiveness of the proposed methods.

APA, Harvard, Vancouver, ISO, and other styles

18

Zhou, Gongjian, Shizhe Bu, and Thiagalingam Kirubarajan. "Simultaneous Spatiotemporal Bias Compensation and Data Fusion for Asynchronous Multisensor Systems." Chinese Journal of Information Fusion 1, no. 1 (2024): 16–32. http://dx.doi.org/10.62762/cjif.2024.361881.

Full text

Abstract:

Bias estimation of sensors is an essential prerequisite for accurate data fusion. Neglect of temporal bias in general real systems prevents the existing algorithms from successful application. In this paper, both spatial and temporal biases in asynchronous multisensor systems are investigated and two novel methods for simultaneous spatiotemporal bias compensation and data fusion are presented. The general situation that the sensors sample at different times with different and varying periods is explored, and unknown time delays may exist between the time stamps and the true measurement times. Due to the time delays, the time stamp interval of the measurements from different sensors may be different from their true measurement interval, and the unknown difference between them is considered as the temporal bias and augmented into the state vector to be estimated. Multisensor measurements are collected in batch processing or sequential processing schemes to estimate the augmented state vector, results in two spatiotemporal bias compensation methods. In both processing schemes, the measurements are formulated as functions of both target states and spatiotemporal biases according to the time difference between the measurements and the states to be estimated. The Unscented Kalman Filter is used to handle the nonlinearity of the measurements and produce spatiotemporal bias and target state estimates simultaneously. The posterior Cramer-Rao lower bound (PCRLB) for spatiotemporal bias and state estimation is presented and simulations are conducted to demonstrate the effectiveness of the proposed methods.

APA, Harvard, Vancouver, ISO, and other styles

19

Liao, Yiwei, Jiangqiong Xie, Zhiguo Wang, and Xiaojing Shen. "Multisensor Estimation Fusion with Gaussian Process for Nonlinear Dynamic Systems." Entropy 21, no. 11 (2019): 1126. http://dx.doi.org/10.3390/e21111126.

Full text

Abstract:

The Gaussian process is gaining increasing importance in different areas such as signal processing, machine learning, robotics, control and aerospace and electronic systems, since it can represent unknown system functions by posterior probability. This paper investigates multisensor fusion in the setting of Gaussian process estimation for nonlinear dynamic systems. In order to overcome the difficulty caused by the unknown nonlinear system models, we associate the transition and measurement functions with the Gaussian process regression models, then the advantages of the non-parametric feature of the Gaussian process can be fully extracted for state estimation. Next, based on the Gaussian process filters, we propose two different fusion methods, centralized estimation fusion and distributed estimation fusion, to utilize the multisensor measurement information. Furthermore, the equivalence of the two proposed fusion methods is established by rigorous analysis. Finally, numerical examples for nonlinear target tracking systems demonstrate the equivalence and show that the multisensor estimation fusion performs better than the single sensor. Meanwhile, the proposed fusion methods outperform the convex combination method and the relaxed Chebyshev center covariance intersection fusion algorithm.

APA, Harvard, Vancouver, ISO, and other styles

20

Ettemeyer, Andreas. "New three-dimensional fiber probe for multisensor coordinate measurement." Optical Engineering 51, no. 8 (2012): 081502. http://dx.doi.org/10.1117/1.oe.51.8.081502.

Full text

APA, Harvard, Vancouver, ISO, and other styles

21

Kim, Hyoung-Won, Do-Hyeung Kim, Hyo-Dal Park, and Taek-Lyul Song. "Multisensor Bias Estimation with Pseudo Measurement for Asynchronous Sensors." Journal of the Korea Institute of Military Science and Technology 14, no. 6 (2011): 1198–206. http://dx.doi.org/10.9766/kimst.2011.14.6.1198.

Full text

APA, Harvard, Vancouver, ISO, and other styles

22

Dou, Yinfeng, Chenjian Ran, and Yuan Gao. "Weighted measurement fusion Kalman estimator for multisensor descriptor system." International Journal of Systems Science 47, no. 11 (2015): 2722–32. http://dx.doi.org/10.1080/00207721.2015.1018368.

Full text

APA, Harvard, Vancouver, ISO, and other styles

23

Harmatys, Wiktor, Adam Gąska, Piotr Gąska, Maciej Gruza, and Jerzy A. Sładek. "Assessment of Background Illumination Influence on Accuracy of Measurements Performed on Optical Coordinate Measuring Machine Equipped with Video Probe." Sensors 21, no. 7 (2021): 2509. http://dx.doi.org/10.3390/s21072509.

Full text

Abstract:

Currently the Coordinate Measuring Technique is facing new challenges both in terms of used methodology and a speed of measurement. More and more often modern optical systems or multisensor systems replace classic solutions. Measurement performed using the optical system is more vulnerable to incorrect points acquisition due to such factors as an inadequate focus or parameters of applied illumination. This article examines the effect of an increasing illumination on the measurement result. A glass reference plate with marked circles and a hole plate standard were used for the measurements performed on a multi-sensor machine Zeiss O’ Inspect 442. The experiment consisted of measurements of standard objects with different values of the backlight at the maximum magnification. Such approach allows to assess the influence of controlled parameter on errors of diameter and form measurements as well as an uncertainty of measurements by determination of ellipses of point repeatability. The analysis of the obtained results shows that increasing backlight mainly affects the result of the diameter measurement.

APA, Harvard, Vancouver, ISO, and other styles

24

Liu, Wen Qiang, Gui Li Tao, Ze Yuan Gu, and Song Li. "Self-Tuning Weighted Measurement Fusion Kalman Signal Filter." Applied Mechanics and Materials 274 (January 2013): 579–82. http://dx.doi.org/10.4028/www.scientific.net/amm.274.579.

Full text

Abstract:

For the single channel autoregressive moving average (ARMA) signals with multisensor and a colored measurement noise, when the model parameters and noise variances are partially unknown, based on identification method and Gevers-Wouters algorithm with a dead band, a self-tuning weighted measurement fusion Kalman signal filter is presented. A simulation example applied to signal processing shows its effectiveness.

APA, Harvard, Vancouver, ISO, and other styles

25

Yu, Weiwei, and Jinming Xing. "Sports Event Level Measurement Indicator System Using Multisensor Information Fusion." Journal of Sensors 2021 (November 1, 2021): 1–9. http://dx.doi.org/10.1155/2021/9330438.

Full text

Abstract:

In view of the imperfection of the measurement index of the level of sports events, the coverage rate of the measurement index is low and the stability is poor. Therefore, this paper puts forward a sports event level measurement index system based on multisensor information fusion. First, the simulated annealing algorithm is used to cluster the grouped sensors and fuse the optimal Bayesian estimation of compatible sensors. Second, the relative entropy measure method is used to expand the compatibility measure of the sensor information in the group, and the optimal Bayesian estimation value of the consistent measure test is obtained. The outliers are eliminated, the optimal fusion value is obtained by the overall weighted statistical fusion, and the alternative measure index system is constructed. Finally, analytic hierarchy process (AHP) is used to calculate the weight of each alternative index, so as to achieve the final measurement index. The results show that the standard deviation of clustering average energy consumption is low, and the energy loss is small. The system can effectively construct the measurement index, and the index coverage rate is as high as 95%.

APA, Harvard, Vancouver, ISO, and other styles

26

Lazić, Žarko, Milče M. Smiljanić, Dragan Tanasković, et al. "Novel MEMS Multisensor Chip for Aerodynamic Pressure Measurements." Sensors 25, no. 3 (2025): 600. https://doi.org/10.3390/s25030600.

Full text

Abstract:

The key equipment for performing aerodynamic testing of objects, such as road and railway vehicles, aircraft, and wind turbines, as well as stationary objects such as bridges and buildings, are multichannel pressure measurement instruments (pressure scanners). These instruments are typically based on arrays of separate pressure sensors built in an enclosure that also contains temperature sensors used for temperature compensation. However, there are significant limitations to such a construction, especially when increasing requirements in terms of miniaturization, the number of pressure channels, and high measurement performance must be met at the same time. In this paper, we present the development and realization of an innovative MEMS multisensor chip, which is designed with the intention of overcoming these limitations. The chip has four MEMS piezoresistive pressure-sensing elements and two resistive temperature-sensing elements, which are all monolithically integrated, enabling better sensor matching and thermal coupling while providing a high number of pressure channels per unit area. The main steps of chip development are preliminary chip design, numerical simulations of the chip’s mechanical behavior when exposed to the measured pressure, final chip design, fabrication processes (photolithography, thermal oxidation, diffusion, layer deposition, micromachining, anodic bonding, and wafer dicing), and electrical testing.

APA, Harvard, Vancouver, ISO, and other styles

27

Alexiev, Kiril, Georgi Shishkov, and Nevena Popova. "Human Activity Registration Using Multisensor Data Fusion." Cybernetics and Information Technologies 15, no. 7 (2015): 99–108. http://dx.doi.org/10.1515/cait-2015-0093.

Full text

Abstract:

Abstract The paper discusses the feasibility of using smart phone devices for human activity registration and analysis. The functional characteristics of the smart phones and their permanent connectivity allow them to serve as a measurement lab and processing unit. An example of using the smart phones as a sensor data source is described, and the corresponding algorithm and results are given. The possible problems are listed and commented.

APA, Harvard, Vancouver, ISO, and other styles

28

Pang, Chongyan, and Shuli Sun. "Fusion Predictors for Multisensor Stochastic Uncertain Systems With Missing Measurements and Unknown Measurement Disturbances." IEEE Sensors Journal 15, no. 8 (2015): 4346–54. http://dx.doi.org/10.1109/jsen.2015.2416511.

Full text

APA, Harvard, Vancouver, ISO, and other styles

29

Stateczny, Andrzej, and Witold Kazimierski. "Multisensor Tracking of Marine Targets - Decentralized Fusion of Kalman and Neural Filters." International Journal of Electronics and Telecommunications 57, no. 1 (2011): 65–70. http://dx.doi.org/10.2478/v10177-011-0009-8.

Full text

Abstract:

Multisensor Tracking of Marine Targets - Decentralized Fusion of Kalman and Neural FiltersThis paper presents an algorithm of multisensor decentralized data fusion for radar tracking of maritime targets. The fusion is performed in the space of Kalman Filter and is done by finding weighted average of single state estimates provided be each of the sensors. The sensors use numerical or neural filters for tracking. The article presents two tracking methods - Kalman Filter and General Regression Neural Network, together with the fusion algorithm. The structural and measurement models of moving target are determined. Two approaches for data fusion are stated - centralized and decentralized - and the latter is thoroughly examined. Further, the discussion on main fusing process problems in complex radar systems is presented. This includes coordinates transformation, track association and measurements synchronization. The results of numerical experiment simulating tracking and fusion process are highlighted. The article is ended with a summary of the issues pointed out during the research.

APA, Harvard, Vancouver, ISO, and other styles

30

Gao, Peng, Hyeonseung Lee, Chan-Woo Jeon, et al. "Improved Position Estimation Algorithm of Agricultural Mobile Robots Based on Multisensor Fusion and Autoencoder Neural Network." Sensors 22, no. 4 (2022): 1522. http://dx.doi.org/10.3390/s22041522.

Full text

Abstract:

High-precision position estimations of agricultural mobile robots (AMRs) are crucial for implementing control instructions. Although the global navigation satellite system (GNSS) and real-time kinematic GNSS (RTK-GNSS) provide high-precision positioning, the AMR accuracy decreases when the signals interfere with buildings or trees. An improved position estimation algorithm based on multisensor fusion and autoencoder neural network is proposed. The multisensor, RTK-GNSS, inertial-measurement-unit, and dual-rotary-encoder data are fused with Extended Kalman filter (EKF). To optimize the EKF noise matrix, the autoencoder and radial basis function (ARBF) neural network was used for modeling the state equation noise and EKF measurement equation. A multisensor AMR test platform was constructed for static experiments to estimate the circular error probability and twice-the-distance root-mean-squared criteria. Dynamic experiments were conducted on road, grass, and field environments. To validate the robustness of the proposed algorithm, abnormal working conditions of the sensors were tested on the road. The results showed that the positioning estimation accuracy was improved compared to the RTK-GNSS in all three environments. When the RTK-GNSS signal experienced interference or rotary encoders failed, the system could still improve the position estimation accuracy. The proposed system and optimization algorithm are thus significant for improving AMR position prediction performance.

APA, Harvard, Vancouver, ISO, and other styles

31

Zettl, Julie D., Mingbin Huang, S. Lee Barbour, and Bing C. Si. "Density-dependent calibration of multisensor capacitance probes in coarse soil." Canadian Journal of Soil Science 95, no. 4 (2015): 331–36. http://dx.doi.org/10.4141/cjss-2015-021.

Full text

Abstract:

Zettl, J. D., Huang, M., Barbour, S. L. and Si, B. C. 2015. Density-dependent calibration of multisensor capacitance probes in coarse soil. Can. J. Soil Sci. 95: 331–336. Coarse-textured reconstructed soils are utilized extensively in the reclamation of mining waste. Accurate and continuous sensing of soil water content is required to understand soil water dynamics and evaluate the hydraulic characteristics of these soils. The EnviroSCAN (Sentek Pty. Ltd, Australia) is a semi-permanent multisensor capacitance probe (MCP) capable of continuous measurement of volumetric water content (θv) and has been used to monitor reclamation soil cover performance. Calibration of these probes is required to improve the accuracy of field measurements. In this study, field and laboratory measurements were undertaken over a range of water contents and bulk densities to refine the relationship between θvand scaled frequency (SF) measured by the MCP. The manufacturer's calibration equation tended to underestimate θvunder wet conditions (θv>0.35 cm3cm–3). Our experimental data showed that bulk density (ρb) did affect the MCP calibration and consequently a new calibration equation that includes the effect of ρbis developed using laboratory measurements and validated using field measurements. This equation provided the highest degree of correlation and the smallest standard deviation of prediction to measured values of θvfor laboratory and field measurements, respectively. This calibration improves the application of the EnviroSCAN for coarse-textured soils such as those utilized in this study.

APA, Harvard, Vancouver, ISO, and other styles

32

Wang, Kuiwu, Qin Zhang, and Xiaolong Hu. "Improved Distributed Multisensor Fusion Method Based on Generalized Covariance Intersection." Journal of Sensors 2022 (October 28, 2022): 1–22. http://dx.doi.org/10.1155/2022/6348938.

Full text

Abstract:

In response to the multitarget tracking problem of distributed sensors with a limited detection range, a distributed sensor measurement complementary Gaussian component correlation GCI fusion tracking method is proposed on the basis of the probabilistic hypothesis density filtering tracking theory. First, the sensor sensing range is extended by complementing the measurements. In this case, the multitarget density product is used to classify whether the measurements belong to the intersection region of the detection range. The local intersection region is complemented only once to reduce the computational cost. Secondly, each sensor runs a probabilistic hypothesis density filter separately and floods the filtering posterior with the neighboring sensors so that each sensor obtains the posterior information of the neighboring sensors. Subsequently, Gaussian components are correlated by distance division, and Gaussian components corresponding to the same target are correlated into the same subset. GCI fusion is performed on each correlated subset to complete the fusion state estimation. Simulation experiments show that the proposed method can effectively perform multitarget tracking in a distributed sensor network with a limited sensing range.

APA, Harvard, Vancouver, ISO, and other styles

33

Liu, Weifeng, Yimei Chen, Chenglin Wen, and Hailong Cui. "A linear multisensor PHD filters via the measurement product space." Journal of Nonlinear Sciences and Applications 10, no. 05 (2017): 2408–22. http://dx.doi.org/10.22436/jnsa.010.05.12.

Full text

APA, Harvard, Vancouver, ISO, and other styles

34

Lozanova, S. V., S. A. Noykov, A. J. Ivanov, and Ch S. Roumenin. "Functional Multisensor for Temperature and Subsequent 3D Magnetic-field Measurement." Procedia Engineering 120 (2015): 824–27. http://dx.doi.org/10.1016/j.proeng.2015.08.683.

Full text

APA, Harvard, Vancouver, ISO, and other styles

35

Palm, M., C. Melsheimer, S. Noël, J. Notholt, J. Burrows, and O. Schrems. "Integrated water vapor above Ny Ålesund, Spitsbergen: a multisensor intercomparison." Atmospheric Chemistry and Physics Discussions 8, no. 6 (2008): 21171–99. http://dx.doi.org/10.5194/acpd-8-21171-2008.

Full text

Abstract:

Abstract. Water vapor is an important constituent of the atmosphere. Because of its abundance it plays an important role for the radiation budget of the atmosphere and has major influence on weather and climate. In this work the integrated water vapor (IWV) measurements derived from the measurements of two satellite sensors, SCIAMACHY and AMSU-B, and two ground-based sensors, a Fourier-transform spectrometer (FTIR) and an O3 microwave ozone sensor (RAM), are compared to radio-sonde measurements in Ny Ålesund, 79° N. All four remote sensors exploit different principles and work in different wavelength regions. Combined they deliver a comprehensive picture of the IWV above Ny Ålesund. The ground-based FTIR reproduces the radio-sonde measurements very well and also shows a high correlation and very little scatter of about 10%. The other remote sensing instruments show a good correlation with the coincident radio-sonde measurements but show high scatter of about 20% (standard deviation). The ground-based RAM performs similar to the satellite instruments, which is somewhat surprising, because measuring IWV is only a by-product for this sensor. The RAM sensor records a measurement every hour and is therefore suited to observe the diurnal variation. As measured by the RAM and FTIR the variance within 4 h is often in excess of 50% (minimum – maximum of the measured IWV). This large variance in the integrated water vapor renders the comparison of different sensors a difficult task. The derived variance of the instruments when compared to radio-sonde measurements can be explained by the high natural variability of IWV.

APA, Harvard, Vancouver, ISO, and other styles

36

Emura, Satoru, and Susumu Tachi. "Multisensor Integrated Prediction for Virtual Reality." Presence: Teleoperators and Virtual Environments 7, no. 4 (1998): 410–22. http://dx.doi.org/10.1162/105474698565811.

Full text

Abstract:

Unconstrained measurement of human head motion is essential for HMDs (headmounted displays) to be really interactive. Polhemus sensors developed for that purpose have deficiencies of critical latency and low sampling rates. Adding to this, a delay for rendering virtual scenes is inevitable. This paper proposes methods that compensate the latency and raises the effective sampling rate by integrating Polhemus and gyro sensors. The adoption of quaternion representation enables us to avoid singularity and the complicated boundary process of rotational motion. The ability of proposed methods under various rendering delays was evaluated in the respect of RMS error and our new correlational technique, which enables us to check the latency and fidelity of a magnetic tracker, and to assess the environment where the magnetic tracker is used. The real-time implementation of our simpler method on personal computers is also reported in detail.

APA, Harvard, Vancouver, ISO, and other styles

37

Kalinichenko, A. A., and L. U. Arseniyeva. "Intelligent Multisensor System For Analytical Control Of Sausages." Methods and Objects of Chemical Analysis 14, no. 2 (2019): 57–72. http://dx.doi.org/10.17721/moca.2019.57-72.

Full text

Abstract:

The new technique of intelligent analysis of chemical aroma patterns of boiled sausages obtained by the electronic nose for authentication and microbiological safety assessment is developed. The informativeness of features extracted from steady-state responses of the multisensor system and robustness of chemometric algorithms for solving the objectives of qualitative and quantitative analysis of sausage volatile compounds are investigated. The classification model was built using maximum response values as input vectors of an optimized probabilistic neural network, which allows obtaining a 100 % accuracy of different sample grades identification and detection samples adulterated with soy protein. The method of partial least squares regression and area values as features were used for regression modelling and prediction of QMAFAnM with a relative error less than 12 % for a microbiological safety assessment of previously identified sausages. The use of the robust analytical technique to assess authentication, adulteration, total bacterial count for one measurement using the electronic nose in combination with machine learning algorithms will allow to significantly reduce the measurement time and the cost of analysis, and avoid subjective estimation of the results.

APA, Harvard, Vancouver, ISO, and other styles

38

Wang, Huadong, and Shi Dong. "Adaptive Fusion Design Using Multiscale Unscented Kalman Filter Approach for Multisensor Data Fusion." Mathematical Problems in Engineering 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/854085.

Full text

Abstract:

In order to improve the reliability of measurement data, the multisensor data fusion technology has progressed greatly in improving the accuracy of measurement data. This paper utilizes the real-time, recursive, and optimal estimation characteristics of unscented Kalman filter (UKF), as well as the unique advantages of multiscale wavelet transform decomposition in data analysis to effectively integrate observational data from multiple sensors. A new multiscale UKF-based multisensor data fusion algorithm is proposed by combining the UKF with multiscale signal analysis. Firstly, model-based UKF is introduced into the multiple sensors, and then the model is decomposed at multiple scales onto the coarse scale with wavelets. Next, signals decomposed from fine to coarse scales are adjusted using the denoised observational data from corresponding sensors and reconstructed with wavelets to obtain the fused signals. Finally, the processed data are fused using adaptive weighted fusion algorithm. Comparison of simulation and experimental results shows that the proposed method can effectively improve the antijamming capability of the measurement system and ensure the reliability and accuracy of sensor measurement system compared to the use of data fusion algorithm alone.

APA, Harvard, Vancouver, ISO, and other styles

39

Zewge, Natnael S., and Hyochoong Bang. "A Distributionally Robust Fusion Framework for Autonomous Multisensor Spacecraft Navigation during Entry Phase of Mars Entry, Descent, and Landing." Remote Sensing 15, no. 4 (2023): 1139. http://dx.doi.org/10.3390/rs15041139.

Full text

Abstract:

A robust multisensor navigation filter design for the entry phase of next-generation Mars entry, descent, and landing (EDL) is presented. The entry phase is the longest and most uncertain portion of a Mars landing sequence. Navigation performance at this stage determines landing precision at the end of the powered descent phase of EDL. In the present work, measurements from a ground-based radio beacon array, an inertial measurement unit (IMU), as well as an array of atmospheric and aerothermal sensors on the body of a Mars entry vehicle are fused using an M-estimation-based iterated extended Kalman filtering (MIEKF) framework. The multisensor approach enables an increased positioning accuracy as well as the estimation of parameters that are otherwise unobservable. Furthermore, owing to the proposed statistically robust filter formulation, states and parameters can be accurately estimated in the presence of non-Gaussian measurement noise. Deviations from normally distributed observation noise correspond to outlier events such as sensor faults or other sources of spurious sensor data such as interference. The proposed framework provides a significant reduction in estimation error at the parachute phase of EDL, thereby increasing the likelihood of a pinpoint landing at a chosen landing site. Six states and three parameters are estimated. The suggested method is compared to the extended Kalman filter (EKF) and the unscented Kalman filter (UKF). Detailed simulation results show that the presented fusion architecture is able to meet future pinpoint planetary landing requirements in realistic sensor measurement scenarios.

APA, Harvard, Vancouver, ISO, and other styles

40

Gao, Song, Min Gao, and Qin Kun Xiao. "Multisensor Tracking of a Maneuvering Target in Clutter with Proposed Parallel Updating Approach." Advanced Materials Research 383-390 (November 2011): 344–51. http://dx.doi.org/10.4028/www.scientific.net/amr.383-390.344.

Full text

Abstract:

To solve the problem of measurement original uncertainty, we present a proposed parallel updating approach for tracking a maneuvering target in cluttered environment using multiple sensors. A parallel updating method is followed where the raw sensor measurements are passed to a central processor and fed directly to the target tracker. A past approach using parallel sensor processing has ignored certain data association probabilities. Simulation results show that compared with an existing IMMPDAF algorithm with parallel sensor approach, the IMMPDAF algorithm with proposed parallel updating approach solves the problem of measurements' origins and achieves significant improvement in the accuracy of track estimation.

APA, Harvard, Vancouver, ISO, and other styles

41

Zhou, Xinliang, and Shantian Wen. "Monitoring and Analysis of Physical Exercise Effects Based on Multisensor Information Fusion." Journal of Sensors 2022 (January 10, 2022): 1–12. http://dx.doi.org/10.1155/2022/4199985.

Full text

Abstract:

In this paper, multiple sensors are used to track human physiological parameters during physical exercise, and data information fusion technology is used to extract useful information for monitoring and analyzing the effects of physical exercise. This paper explores the interaction and developmental dynamics of multisensor information fusion technology and physical exercise data monitoring based on the interrelationship and interpenetration between the two. The design ideas and principles that should be followed for the software designed in this study are discussed from the perspective of the portable design of measurement instruments and the perspective of multisensor information fusion, and then, the overall architecture and each functional module are studied to propose a scientific and reasonable design model. The general methodological model to be followed for the development of this resource is designed, and the basic development process of the model is explained and discussed, especially the requirement analysis and structural design, and how to build the development environment are explained in detail; secondly, based on the course unit development process in this model, we clarify the limitations of the system through meticulous analysis of the measurement results, which provides a solid foundation for the next step of system optimization. Finally, with a focus on future development, we elaborate on the potential possible role and development trend of multisensor information fusion in the future period. In this paper, we propose to apply the multisensor data fusion algorithm to the monitoring, analysis, and evaluation of the effect of physical exercise, by collecting multiple human physiological parameters during physical exercise through multiple sensors and performing data fusion processing on the collected physiological parameters to finally evaluate the effect of physical exercise.

APA, Harvard, Vancouver, ISO, and other styles

42

Yin, Hao, Dongguang Li, Yue Wang, and Xiaotong Hong. "Adaptive Data Fusion Method of Multisensors Based on LSTM-GWFA Hybrid Model for Tracking Dynamic Targets." Sensors 22, no. 15 (2022): 5800. http://dx.doi.org/10.3390/s22155800.

Full text

Abstract:

In preparation for the battlefields of the future, using unmanned aerial vehicles (UAV) loaded with multisensors to track dynamic targets has become the research focus in recent years. According to the air combat tracking scenarios and traditional multisensor weighted fusion algorithms, this paper contains designs of a new data fusion method using a global Kalman filter and LSTM prediction measurement variance, which uses an adaptive truncation mechanism to determine the optimal weights. The method considers the temporal correlation of the measured data and introduces a detection mechanism for maneuvering of targets. Numerical simulation results show the accuracy of the algorithm can be improved about 66% by training 871 flight data. Based on a mature refitted civil wing UAV platform, the field experiments verified the data fusion method for tracking dynamic target is effective, stable, and has generalization ability.

APA, Harvard, Vancouver, ISO, and other styles

43

Duda, Zdzisław. "Fusion Kalman filtration for distributed multisensor systems." Archives of Control Sciences 24, no. 1 (2014): 53–65. http://dx.doi.org/10.2478/acsc-2014-0004.

Full text

Abstract:

Abstract In the paper, fusion state hierarchical filtration for a multisensor system is considered. An optimal global Kalman filter is realized by a central node in the information form. The state estimate depends on local information that should be sent by local nodes. Two information structures are considered in the paper. In the first case local estimates are based on the local measurement information. It leads to distributed Kalman filter fusion that is well known in a literature. In the second case local node has additionally global information of the system with one step delay. A synthesis of local filters is presented. An advantage of this structure is discussed.

APA, Harvard, Vancouver, ISO, and other styles

44

Zong, Hua, Zhaohui Gao, Wenhui Wei, Yongmin Zhong, and Chengfan Gu. "Randomly Weighted CKF for Multisensor Integrated Systems." Journal of Sensors 2019 (November 11, 2019): 1–19. http://dx.doi.org/10.1155/2019/1216838.

Full text

Abstract:

The cubature Kalman filter (CKF) is an estimation method for nonlinear Gaussian systems. However, its filtering solution is affected by system error, leading to biased or diverged system state estimation. This paper proposes a randomly weighted CKF (RWCKF) to handle the CKF limitation. This method incorporates random weights in CKF to restrain system error’s influence on system state estimation by dynamic modification of cubature point weights. Randomly weighted theories are established to estimate predicted system state and system measurement as well as their covariances. Simulation and experimental results as well as comparison analyses demonstrate the presented RWCKF conquers the CKF problem, leading to enhanced accuracy for system state estimation.

APA, Harvard, Vancouver, ISO, and other styles

45

Zaborowski, Wiesław, Wiktor Harmatys, and Adam Gąska. "The Importance of Differences in Results Obtained from Measurements with Various Measuring Systems and Measuring Modes in Industrial Practice." Applied Sciences 12, no. 23 (2022): 12412. http://dx.doi.org/10.3390/app122312412.

Full text

Abstract:

This paper presents the results of preliminary tests carried out in an organization producing products for the automotive industry. From the many available systems used in this research, different values of results were obtained; these differences cause doubts among people deciding about the process approval and start of production. The main aim of the research presented in the article is to determine the influence of various factors on the measurement results, especially to compare the results of measurements obtained with the optical sensor, which is used during measurements with the use of a multisensor measuring machine. The results obtained with the use of the height gauge, which is used alternatively in the organization, raise further doubts. Experience has shown that the methodology and definition of the alignment during the measurement, which is different for each of the systems, have a great influence.

APA, Harvard, Vancouver, ISO, and other styles

46

Weidong, Gao, and Zhao Zhenwei. "Gait Phase Recognition Using Fuzzy Logic Regulation with Multisensor Data Fusion." Journal of Sensors 2021 (September 14, 2021): 1–13. http://dx.doi.org/10.1155/2021/8776059.

Full text

Abstract:

The health challenges brought by aging population and chronic noncommunicable diseases are increasingly severe. Scientific physical exercise is of great significance to prevent the occurrence of chronic diseases and subhealth intervention and promote health. However, improper or excessive exercise can cause injury. Research shows that the sports injury rate of people who often exercise is as high as 85%. Aiming at the problem of low accuracy of single sensor gait analysis, a real-time gait detection algorithm based on piezoelectric film and motion sensor is proposed. On this basis, a gait phase recognition method based on fuzzy logic is proposed, which enhances the ability of gait space-time measurement. Experimental results show that the proposed gait modeling method based on ground reaction force (GRF) signal can effectively recognize and quantify various gait patterns. At the same time, the introduction of heterogeneous sensor data fusion technology can effectively make up for the accuracy defects of single sensor measurement and improve the estimation accuracy of gait space-time measurement.

APA, Harvard, Vancouver, ISO, and other styles

47

Wang, Xin, Shu-Li Sun, Kai-Hui Ding, and Jing-Yan Xue. "Weighted Measurement Fusion White Noise Deconvolution Filter with Correlated Noise for Multisensor Stochastic Systems." Mathematical Problems in Engineering 2012 (2012): 1–16. http://dx.doi.org/10.1155/2012/257619.

Full text

Abstract:

For the multisensor linear discrete time-invariant stochastic control systems with different measurement matrices and correlated noises, the centralized measurement fusion white noise estimators are presented by the linear minimum variance criterion under the condition that noise input matrix is full column rank. They have the expensive computing burden due to the high-dimension extended measurement matrix. To reduce the computing burden, the weighted measurement fusion white noise estimators are presented. It is proved that weighted measurement fusion white noise estimators have the same accuracy as the centralized measurement fusion white noise estimators, so it has global optimality. It can be applied to signal processing in oil seismic exploration. A simulation example for Bernoulli-Gaussian white noise deconvolution filter verifies the effectiveness.

APA, Harvard, Vancouver, ISO, and other styles

48

Piotr, Borowik, Adamowicz Leszek, Tarakowski Rafał, Siwek Krzysztof, and Grzywacz Tomasz. "On data collection time by an electronic nose." International Journal of Electrical and Computer Engineering (IJECE) 11, no. 6 (2021): 4767–73. https://doi.org/10.11591/ijece.v11i6.pp4767-4773.

Full text

Abstract:

We use electronic nose data of odor measurements to build machine learning clas- sification models. The presented analysis focused on determining the optimal time of measurement, leading to the best model performance. We observe that the most valuable information for classification is available in data collected at the beginning of adsorption and the beginning of the desorption phase of measurement. We demonstrated that the usage of complex features extracted from the sensors’ response gives better classification performance than use as features only raw values of sensors’ response, normalized by baseline. We use a group shuffling cross-validation approach for determining the reported models’ average accuracy and standard deviation.

APA, Harvard, Vancouver, ISO, and other styles

49

Vlasov, Yu, A. Legin, A. Rudnitskaya, C. Di Natale, and A. D'Amico. "Nonspecific sensor arrays ("electronic tongue") for chemical analysis of liquids (IUPAC Technical Report)." Pure and Applied Chemistry 77, no. 11 (2005): 1965–83. http://dx.doi.org/10.1351/pac200577111965.

Full text

Abstract:

The history of the development of potentiometric sensors over the past century demonstrates progress in constructing single, discrete (i.e., separate, to distinguish from sensor arrays) ion sensors, which have been made as selective as possible. Only a few types reveal high selectivity. However, easy measurement procedure, with low cost and availability, give rise to the search for new ways for their successful application. The present document describes a new concept for application of potentiometric multisensor systems, viz., sensor arrays for solution analysis, and the performance of this new analytical tool - the "electronic tongue". The electronic tongue is a multisensor system, which consists of a number of low-selective sensors and uses advanced mathematical procedures for signal processing based on the pattern recognition (PARC) and/or multivariate analysis [artificial neural networks (ANNs), principal component analysis (PCA), etc.]. Definitions of the multisensor systems and their parameters are suggested. Results from the application of the electronic tongue, both for quantitative and qualitative analysis of different mineral water and wine samples, are presented and discussed.

APA, Harvard, Vancouver, ISO, and other styles

50

Zhang, Ming Bo, and Xiao Jun Sun. "Identification Method for Multichannel Multisensor ARMA Signal with Colored Measurement Noises." Applied Mechanics and Materials 220-223 (November 2012): 1922–28. http://dx.doi.org/10.4028/www.scientific.net/amm.220-223.1922.

Full text

Abstract:

For the multisensor multichannel ARMA signal with ARMA colored measurement noises and unknown model parameters and noise variances, this paper presents a kind of multi-stage identification method. At the first stage, the on-line information fusion estimator for the unknown model parameters is presented based on the Recursive Instrumental Variable (RIV) algorithm and the Recursive Extended Least Squares (RELS) algorithm, which is realized by computing the average of local estimators for model parameter. At the second stage, the on-line information fusion estimator for the unknown variances is obtained using the correlation method, which is realized by computing the average of the local estimators for noise variances. At the third stage, the information fusion parameter estimator of MA model is presented using the correlation method and the dead zone Gevers-Wouters and LS algorithms.

APA, Harvard, Vancouver, ISO, and other styles

Journal articles on the topic 'Multisensor measurement'

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles