Conferences related to Multisensor systems

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2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA )

ETFA focus is on the latest developments and new technologies in the field of industrial and factory automation. The conference aims to exchange ideas with both industry leaders and a variety of experienced researchers, developers, and practitioners from several industries, research institutes, and academia


2021 IEEE International Conference on Mechatronics (ICM)

CM focuses on recent developments and future prospects related to the synergetic integration of mechanics, electronics, and information processing.


2020 IEEE 23rd International Conference on Information Fusion (FUSION)

The International Conference on Information Fusion is the premier forum for interchange of the latest research in data and information fusion, and its impacts on our society. The conference brings together researchers and practitioners from academia and industry to report on the latest scientific and technical advances.


GLOBECOM 2020 - 2020 IEEE Global Communications Conference

IEEE Global Communications Conference (GLOBECOM) is one of the IEEE Communications Society’s two flagship conferences dedicated to driving innovation in nearly every aspect of communications. Each year, more than 2,900 scientific researchers and their management submit proposals for program sessions to be held at the annual conference. After extensive peer review, the best of the proposals are selected for the conference program, which includes technical papers, tutorials, workshops and industry sessions designed specifically to advance technologies, systems and infrastructure that are continuing to reshape the world and provide all users with access to an unprecedented spectrum of high-speed, seamless and cost-effective global telecommunications services.


2020 IEEE International Conference on Industrial Technology (ICIT)

ICIT focuses on industrial and manufacturing applications of electronics, controls, communications, instrumentation, and computational intelligence.



Periodicals related to Multisensor systems

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Aerospace and Electronic Systems Magazine, IEEE

The IEEE Aerospace and Electronic Systems Magazine publishes articles concerned with the various aspects of systems for space, air, ocean, or ground environments.


Automatic Control, IEEE Transactions on

The theory, design and application of Control Systems. It shall encompass components, and the integration of these components, as are necessary for the construction of such systems. The word `systems' as used herein shall be interpreted to include physical, biological, organizational and other entities and combinations thereof, which can be represented through a mathematical symbolism. The Field of Interest: shall ...


Biomedical Circuits and Systems, IEEE Transactions on

The Transactions on Biomedical Circuits and Systems addresses areas at the crossroads of Circuits and Systems and Life Sciences. The main emphasis is on microelectronic issues in a wide range of applications found in life sciences, physical sciences and engineering. The primary goal of the journal is to bridge the unique scientific and technical activities of the Circuits and Systems ...


Communications Magazine, IEEE

IEEE Communications Magazine was the number three most-cited journal in telecommunications and the number eighteen cited journal in electrical and electronics engineering in 2004, according to the annual Journal Citation Report (2004 edition) published by the Institute for Scientific Information. Read more at http://www.ieee.org/products/citations.html. This magazine covers all areas of communications such as lightwave telecommunications, high-speed data communications, personal communications ...


Computer

Computer, the flagship publication of the IEEE Computer Society, publishes peer-reviewed technical content that covers all aspects of computer science, computer engineering, technology, and applications. Computer is a resource that practitioners, researchers, and managers can rely on to provide timely information about current research developments, trends, best practices, and changes in the profession.



Most published Xplore authors for Multisensor systems

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Xplore Articles related to Multisensor systems

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Multisensor Information Fusion White Noise Estimator

2006 6th World Congress on Intelligent Control and Automation, 2006

Using the modern time series analysis method and white noise estimation theory, under the linear minimum variance optimal information fusion criterion, for multisensor systems with colored measurement noise, the multisensor optimal information fusion white noise deconvolution Wiener filter is presented, which consists of weighting local white noise deconvolution Wiener filters. The formula of computing covariances among the local filtering errors ...


Information fusion estimation of noise statistics for multisensor systems

2009 Chinese Control and Decision Conference, 2009

For multisensor linear discrete time invariant system with unknown noise statistics and correlated noises, by the correlation method, the online local estimators of noise variances, correlated matrices and cross covariances can be obtained by solving the different partial correlated function matrix equations. The information fusion noise statistics estimators are presented by averaging the local estimators of noise statistics. Based on ...


Distributed multiple hypotheses testing with serial distributed decision fusion

Proceedings 2001 IEEE International Symposium on Computational Intelligence in Robotics and Automation (Cat. No.01EX515), 2001

In this paper we present a multiple hypothesis testing strategy for serial (so-called tandem) distributed decision networks. Comparing with parallel decision networks, some nice properties of tandem networks are also of interest. Applying our flexible multiple Hypotheses Testing methods to serial distributed multisensor systems, the decision risk functions are provided, the decision rules for each individual sensor (viewed as middle ...


Multisensor fusion using neural networks

Proceedings of the Second IEEE Symposium on Parallel and Distributed Processing 1990, 1990

A multisensor system for robot navigation has been developed that uses artificial neural networks to perform sensor data fusion. Four neural networks were investigated regarding their potential to fuse data from an ultrasonic and an infrared range finder to yield more accurate estimates of depth. A radial basis predicter using localized receptive fields (LRF) was able to learn mappings quickly. ...


State estimation with feedback information in a hybrid multisensor system

2001 CIE International Conference on Radar Proceedings (Cat No.01TH8559), 2001

In order to improve the tracking performance of sensors, this paper addresses the issue of optimal state estimation in a hybrid multisensor data fusion system with feedback information for similar and dissimilar synchronous sensors. In this system, part of the sensors process their data locally to produce local tracks while another part of the sensors only provide detection reports. These ...



Educational Resources on Multisensor systems

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IEEE.tv Videos

Non-Invasive Techniques for Monitoring Chronic Heart Failure - Harry Silber - IEEE EMBS at NIH, 2019
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Impact on Society: Systems Engineer to Systems Entrepreneur for Global Change - Erna Grasz at the 2017 IEEE VIC Summit
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EMBC 2011-Program-Systems in Synthetic Biology (Part I)-Pamela A. Silver
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IROS TV 2019- Maryland Robotics Center, Institute for Systems Research, University of Maryland
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Wireless Charging Systems for EVs
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Research, Development and Field Test of Robotic Observation Systems for Active Volcanic Areas in Japan

IEEE-USA E-Books

  • Multisensor Information Fusion White Noise Estimator

    Using the modern time series analysis method and white noise estimation theory, under the linear minimum variance optimal information fusion criterion, for multisensor systems with colored measurement noise, the multisensor optimal information fusion white noise deconvolution Wiener filter is presented, which consists of weighting local white noise deconvolution Wiener filters. The formula of computing covariances among the local filtering errors is presented, and the formula of optimal weighting coefficients is also given. Compared with the single sensor case, the accuracy of the fused filter is improved. It can be applied to signal processing in oil seismic exploration. A simulation example for information fusion Bernoulli-Gaussian white noise deconvolution filter shows its effectiveness

  • Information fusion estimation of noise statistics for multisensor systems

    For multisensor linear discrete time invariant system with unknown noise statistics and correlated noises, by the correlation method, the online local estimators of noise variances, correlated matrices and cross covariances can be obtained by solving the different partial correlated function matrix equations. The information fusion noise statistics estimators are presented by averaging the local estimators of noise statistics. Based on the ergodicity of the sample correlated function, it is proved the local and fused estimators of noise statistics are strong consistent, i.e. they converge to corresponding true values with probability one. They can be applied to design the self tuning information fusion filters. A simulation example of three-sensor system with correlated noises shows the effectiveness of the fused estimation.

  • Distributed multiple hypotheses testing with serial distributed decision fusion

    In this paper we present a multiple hypothesis testing strategy for serial (so-called tandem) distributed decision networks. Comparing with parallel decision networks, some nice properties of tandem networks are also of interest. Applying our flexible multiple Hypotheses Testing methods to serial distributed multisensor systems, the decision risk functions are provided, the decision rules for each individual sensor (viewed as middle nodes) as well as fusion sensor (as final node) are derived. It is also shown how to compute sensor performance and system performance in terms of variety of probability masses. A numerical example is given to demonstrate the efficacy of the proposed method.

  • Multisensor fusion using neural networks

    A multisensor system for robot navigation has been developed that uses artificial neural networks to perform sensor data fusion. Four neural networks were investigated regarding their potential to fuse data from an ultrasonic and an infrared range finder to yield more accurate estimates of depth. A radial basis predicter using localized receptive fields (LRF) was able to learn mappings quickly. However, it failed to locate receptive fields correctly within the input space thus providing a poorer mapping than a backpropagation network. A variation on LRF incorporating dynamic node creation was able to learn good mappings in about the same amount of time as a backprop network while exploring different network sizes. An output encoding scheme produced the best performance by exhibiting less error at places where the depth functions that varied rapidly. The resulting network provides a cost-effective solution to range estimation for autonomous navigation using on-board hardware.<<ETX>>

  • State estimation with feedback information in a hybrid multisensor system

    In order to improve the tracking performance of sensors, this paper addresses the issue of optimal state estimation in a hybrid multisensor data fusion system with feedback information for similar and dissimilar synchronous sensors. In this system, part of the sensors process their data locally to produce local tracks while another part of the sensors only provide detection reports. These tracks and detection reports are communicated to a central site where track fusion and state estimation are performed. The fusion results are then returned to some sensors, so that the tracking performance of the sensors might be improved.

  • Optimal N-P detection for multisensor system without high-dimensional integral

    Consider detection problem for multisensor system with Gaussian measurements. According to the NeymanPearson criterion, the detection probability was maximized subject to an upper limit on the probability of false alarm. In this case, it is well known that the optimal test admits the likelihood-ratio test (LRT) form. However the threshold computation of LRT is generally computationally intractable because of the high-dimensional integral caused by the multisensor measurements. In this paper, we equivalently transform the LRT into semidefinite positive quadratic form, whose distribution can be obtained by recursive formula of a series. More importantly, using such technique, the high-dimensional integral computation can be reduced to one dimensional iterative computation. Therefore, our method can greatly reduce computational burden. Numerical examples support the above analysis.

  • A smart sensor based visual landmarks detection for indoor robot navigation

    In this article we present a multisensor system for usual landmarks detection in indoor autonomous robot navigation context. This smart sensor combines a CCD camera with a 3D laser camera. The landmarks detection is based on the collaboration between intensity image processing which ensures the planar quadrangle landmarks detection itself and ad hoc 3D laser camera which copes with the specific constraints of our problematic. The process consists in grouping horizontally/vertically oriented segments extracted from the intensity image. The constraints are applied thanks to a relaxation scheme. The next step consists in confirming or rejecting the detected quadrangles regarding their planarity by focusing the 3D laser camera in the environment area defined by these visual targets.

  • Self-tuning Measurement Fusion Kalman Predictor

    For the multisensor system with unknown noise variances and with the different measurement matrices which have the same right common factor, based on the solution of the matrix equations for correlation function, the on-line estimators of the noise variance matrices are obtained. Further, a self-tuning weighted measurement fusion Kalman predictor is presented based on the Riccati equation. By using the dynamic error system analysis method, it is strictly proved that it converges to the steady state globally optimal fusion Kalman predictor in a realization or with probability one, so that it has asymptotic global optimality. A simulation example for a target tracking system with 3-sensor shows its effectiveness.

  • Position estimation of mobile robots based on coded infrared signal transmission

    A system based on coded infrared signal transmission for the estimation of position of mobile robots in a structured environment is reported. Particular emphasis is placed on the polar coordinate arrangement in which signals are sent from the transmitters situated at the corners of the boundaries of operation. A multisensor system, strategically situated onboard the robot, has been found to improve the accuracy of the position estimation substantially. The information detected by the sensors is suitably processed to calculate the central position of the robot geometrically. The algorithms for the position calculations and the operational strategy are presented. This system forms the basis for the coordination and cooperation philosophy of multiple mobile robots sharing the same environment and performing cooperative or competitive tasks.

  • Improving navigation using active landmark recognition

    Navigation of underwater vehicles is a very demanding task, especially in confined environments. The risk of damage, expense and sometimes danger of recovering a vehicle makes improving the system's awareness of its environment a research priority. We propose here a novel approach for underwater navigation systems that combines data fusion from a variety of conventional on-board sensors (such as heading, depth, tilt, short-baseline sonar positioning), with an active vision system to perform attentive landmark recognition using optical and sonar imagery.



Standards related to Multisensor systems

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IEEE Standard for Inertial Sensor Terminology

To review all of the definitions included in the standard and to revise them as required. New terminology will be added to bring the document up to date with current technology.