Covariance matrix

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In probability theory and statistics, a covariance matrix (also known as dispersion matrix) is a matrix whose element in the i, j position is the covariance between the i and j elements of a random vector. (Wikipedia.org)






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2019 IEEE 58th Conference on Decision and Control (CDC)

The CDC is recognized as the premier scientific and engineering conference dedicated to the advancement of the theory and practice of systems and control. The CDC annually brings together an international community of researchers and practitioners in the field of automatic control to discuss new research results, perspectives on future developments, and innovative applications relevant to decision making, systems and control, and related areas.The 58th CDC will feature contributed and invited papers, as well as workshops and may include tutorial sessions.The IEEE CDC is hosted by the IEEE Control Systems Society (CSS) in cooperation with the Society for Industrial and Applied Mathematics (SIAM), the Institute for Operations Research and the Management Sciences (INFORMS), the Japanese Society for Instrument and Control Engineers (SICE), and the European Union Control Association (EUCA).


2019 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

International Geosicence and Remote Sensing Symposium (IGARSS) is the annual conference sponsored by the IEEE Geoscience and Remote Sensing Society (IEEE GRSS), which is also the flagship event of the society. The topics of IGARSS cover a wide variety of the research on the theory, techniques, and applications of remote sensing in geoscience, which includes: the fundamentals of the interactions electromagnetic waves with environment and target to be observed; the techniques and implementation of remote sensing for imaging and sounding; the analysis, processing and information technology of remote sensing data; the applications of remote sensing in different aspects of earth science; the missions and projects of earth observation satellites and airborne and ground based campaigns. The theme of IGARSS 2019 is “Enviroment and Disasters”, and some emphases will be given on related special topics.


2019 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting

The conference is intended to provide an international forum for the exchange of information on state-of-the-art research in antennas, propagation, electromagnetics, and radio science.


ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

ICASSP is the world’s largest and most comprehensive technical conference focused on signal processing and its applications. The conference will feature world-class presentations by internationally renowned speakers, cutting-edge session topics and provide a fantastic opportunity to network with like-minded professionals from around the world.


ICC 2019 - 2019 IEEE International Conference on Communications (ICC)

The 2019 IEEE International Conference on Communications (ICC) will be held from 20-24 May 2019 at Shanghai International Convention Center, China,conveniently located in the East Coast of China, the region home to many of the world’s largest ICT industries and research labs. Themed“Smart Communications”, this flagship conference of IEEE Communications Society will feature a comprehensive Technical Program including16 Symposia and a number of Tutorials and Workshops. IEEE ICC 2019 will also include an attractive Industry Forum & Exhibition Program featuringkeynote speakers, business and industry pan


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Audio, Speech, and Language Processing, IEEE Transactions on

Speech analysis, synthesis, coding speech recognition, speaker recognition, language modeling, speech production and perception, speech enhancement. In audio, transducers, room acoustics, active sound control, human audition, analysis/synthesis/coding of music, and consumer audio. (8) (IEEE Guide for Authors) The scope for the proposed transactions includes SPEECH PROCESSING - Transmission and storage of Speech signals; speech coding; speech enhancement and noise reduction; ...


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 Engineering, IEEE Transactions on

Broad coverage of concepts and methods of the physical and engineering sciences applied in biology and medicine, ranging from formalized mathematical theory through experimental science and technological development to practical clinical applications.


Communications Letters, IEEE

Covers topics in the scope of IEEE Transactions on Communications but in the form of very brief publication (maximum of 6column lengths, including all diagrams and tables.)


Communications, IEEE Transactions on

Telephone, telegraphy, facsimile, and point-to-point television, by electromagnetic propagation, including radio; wire; aerial, underground, coaxial, and submarine cables; waveguides, communication satellites, and lasers; in marine, aeronautical, space and fixed station services; repeaters, radio relaying, signal storage, and regeneration; telecommunication error detection and correction; multiplexing and carrier techniques; communication switching systems; data communications; and communication theory. In addition to the above, ...


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Xplore Articles related to Covariance matrix

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Some comments to application of Cognition principle in Wireless Networks

European Wireless 2012; 18th European Wireless Conference 2012, 2012

Most of the publications related to Cognitive Radio (CR) Wireless Networks are devoted mainly to Spectrum Sensing problems and Resource Allocation for Cognitive Users (SU). So far, there is a high grade of understanding on how to provide solutions for the above mentioned problems as well as for the problems related to them.


Signal Enhancement as Minimization of Relevant Information Loss

SCC 2013; 9th International ITG Conference on Systems, Communication and Coding, 2013

We introduce the notion of relevant information loss for the purpose of casting the signal enhancement problem in information-theoretic terms. We show that many algorithms from machine learning can be reformulated using relevant information loss, which allows their application to the aforementioned problem. As a particular example we analyze principle component analysis for dimensionality reduction, discuss its optimality, and show ...


Reduced-order adaptive Kalman filtering for dual-frequency navigation with carrier phase

2011 11th International Symposium on Communications & Information Technologies (ISCIT), 2011

A new adaptive Kalman filtering algorithm for dual-frequency navigation with carrier phase is presented. By reducing the filtering order after full-order initialization, this algorithm saves the extra computational cost brought by carrier phase observations. The improved adaptation of state covariance matrix in reduced-order processing also improves filtering precision and robustness. Finally applications on both static data and kinetic simulations demonstrate ...


Sensitivity of system performance to individual error statistics

1980 19th IEEE Conference on Decision and Control including the Symposium on Adaptive Processes, 1980

The method of equivalent observation 1 is extended to vector valued constant parameters and to uncorrelated measurement noise sequences. It is then used to determine easily calculated partial derivatives of final covariances with respect to parameter variances. Error matrices and bounds are developed in order to determine the range over which linearity may be assumed.


Shape calibration for a nominally linear equispaced array

1993 IEEE International Conference on Acoustics, Speech, and Signal Processing, 1993

The author considers a thin flexible line array of equispaced hydrophones that is towed through the sea, and develops a procedure that allows testing of the straightness of the array. The motion of the towing ship, the currents of the ocean and other forces induce deformations on the array and affect the performance of spatial processing of the data developed ...


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Educational Resources on Covariance matrix

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IEEE-USA E-Books

  • Some comments to application of Cognition principle in Wireless Networks

    Most of the publications related to Cognitive Radio (CR) Wireless Networks are devoted mainly to Spectrum Sensing problems and Resource Allocation for Cognitive Users (SU). So far, there is a high grade of understanding on how to provide solutions for the above mentioned problems as well as for the problems related to them.

  • Signal Enhancement as Minimization of Relevant Information Loss

    We introduce the notion of relevant information loss for the purpose of casting the signal enhancement problem in information-theoretic terms. We show that many algorithms from machine learning can be reformulated using relevant information loss, which allows their application to the aforementioned problem. As a particular example we analyze principle component analysis for dimensionality reduction, discuss its optimality, and show that the relevant information loss can indeed vanish if the relevant information is concentrated on a lower-dimensional subspace of the input space.

  • Reduced-order adaptive Kalman filtering for dual-frequency navigation with carrier phase

    A new adaptive Kalman filtering algorithm for dual-frequency navigation with carrier phase is presented. By reducing the filtering order after full-order initialization, this algorithm saves the extra computational cost brought by carrier phase observations. The improved adaptation of state covariance matrix in reduced-order processing also improves filtering precision and robustness. Finally applications on both static data and kinetic simulations demonstrate the validity and efficiency of the algorithm.

  • Sensitivity of system performance to individual error statistics

    The method of equivalent observation 1 is extended to vector valued constant parameters and to uncorrelated measurement noise sequences. It is then used to determine easily calculated partial derivatives of final covariances with respect to parameter variances. Error matrices and bounds are developed in order to determine the range over which linearity may be assumed.

  • Shape calibration for a nominally linear equispaced array

    The author considers a thin flexible line array of equispaced hydrophones that is towed through the sea, and develops a procedure that allows testing of the straightness of the array. The motion of the towing ship, the currents of the ocean and other forces induce deformations on the array and affect the performance of spatial processing of the data developed under the assumption that the array is straight. When the ship is maneuvering, the processing is generally turned off for long periods of time, an extremely penalizing situation that can be overcome by applying the procedure described to determine the maximum size of admissible sub-arrays on which the standard processing can be pursued.<<ETX>>

  • Array processing with neural networks for estimation of near-field source position

    The authors propose an implementation method of a neural network for an estimation problem of near-field source positions. This method can estimate not only the positions but also the power of the sources. Its resolving power is higher than that of the backward propagation method. In comparison with the maximum entropy method, the present method can estimate source positions in noisier environments. Simulation results are presented.<<ETX>>

  • Optimal subarray size for spatial smoothing

    We consider the popular spatial smoothing technique and show via the covariance matrix eigenvalue analysis that the simple suboptimal rule for choosing of the subarray size exists in a practically important situation of two coherent equipower closely spaced sources. This rule has been derived by maximizing the distance between the signal subspace and the noise subspace eigenvalues of spatially smoothed covariance matrix and it does not require any a priori information about the signal source parameters.<<ETX>>

  • Bayesian Estimation With Distance Bounds

    We consider the problem of estimating a random state vector when there is information about the maximum distances between its subvectors. The estimation problem is posed in a Bayesian framework in which the minimum mean square error (MMSE) estimate of the state is given by the conditional mean. Since finding the conditional mean requires multidimensional integration, an approximate MMSE estimator is proposed. The performance of the proposed estimator is evaluated in a positioning problem. Finally, the application of the estimator in inequality constrained recursive filtering is illustrated by applying the estimator to a dead-reckoning problem. The MSE of the estimator is compared with two related posterior Cramér-Rao bounds.

  • Properties of FET parameter statistical data bases

    Statistical databases are often used to characterize the statistics of a FET. It is shown that a database containing the FET model parameter marginal probability density functions and covariance matrix is not sufficient to describe the FET's S-parameter statistics. This result is important to those developing statistical databases for GaAs FETs. The implications of this work for simulation and CAD are discussed, and a solution to this problem, the truth model, is presented.<<ETX>>

  • An adaptive State estimator for pulverizer control using moments of particle size distribution

    An adaptive state estimator for pulverizers consisting of blending, grinding, and classifying processes has been developed in order to improve control of pulverized-coal-fired power stations. Though coal flow and non-Gaussian particle size distributions in the processes are mutually related, the estimator is able to efficiently simulate flow and normalized moments of the distributions with a state vector. The estimator also identifies coal grindability for adapting to variation in coal characteristic in parallel with the process simulation. The accuracy of the adaptive estimation and the effectiveness in improving the load-swinging performance have been validated at a 1000-MWe class power station.



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