The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Wim Desmet - One of the best experts on this subject based on the ideXlab platform.
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Joint State input estimation with a fourier dictionary for the input representation effect of spectral leakage
Journal of Physics: Conference Series, 2019Co-Authors: Matteo Kirchner, Jan Croes, Wim DesmetAbstract:The compressive sensing-moving horizon estimator (CS-MHE) is an approach for Joint State/input estimation. It integrates compressive sensing principles into a moving horizon estimator, enabling to exploit shape functions to model an input, resulting in better observability and wider input bandwidth in comparison to other input models. With the final aim of using the CS-MHE for the estimation of forces and torques in rotating machinery which exhibit some form of periodicity, the authors have recently investigated Fourier shape functions. A first experimental validation showed very accurate estimation under the hypothesis of no spectral leakage, i.e., the MHE window, the sampling rate and the Fourier dictionary match a known input periodicity, with the input consisting of few sinusoidal components. This paper discusses the problem of spectral leakage that can result if the MHE window does not match the signal periodicity. In particular, we show how to remove the link between the MHE window and a Fourier dictionary, we discuss how the autocorrelation can be employed to detect the periodicity and we list further possible alternatives to enhance a sparse solution. The discussion is supported by numerical and experimental investigations.
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kalman based load identification and full field estimation analysis on industrial test case
Mechanical Systems and Signal Processing, 2019Co-Authors: Roberta Cumbo, Tommaso Tamarozzi, Karl Janssens, Wim DesmetAbstract:Abstract The potential of the Augmented Kalman Filter algorithm is tested in this paper for Joint State-input estimation in structural dynamics field. In view of inverse load identification, the filter is compared with the Transfer Path Analysis Matrix Inversion technique, commonly used for industrial applications. An existing Optimal Sensor Placement strategy for Kalman Filter is adopted and validated on real experimental data. The advantages of the proposed methods, through strain measurements information, are identified in the effort needed for data-acquisition and data-processing. The effectiveness of the filter and the quality of the results are demonstrated in this paper for an industrial test-case, such as a rear twistbeam suspension.
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exploiting input sparsity for Joint State input moving horizon estimation
Mechanical Systems and Signal Processing, 2018Co-Authors: Matteo Kirchner, Jan Croes, Francesco Cosco, Wim DesmetAbstract:Abstract This paper proposes a novel time domain approach for Joint State/input estimation of mechanical systems. The novelty consists of exploiting compressive sensing (CS) principles in a moving horizon estimator (MHE), allowing the observation of a large number of input locations given a small set of measurements. Existing techniques are characterized by intrinsic limitations when estimating multiple input locations, due to an observability decrease. Moreover, CS does not require an input to be characterized by a slow dynamics, which is a requirement of other State of the art techniques for input modeling. In the new approach, called compressive sensing–moving horizon estimator (CS-MHE), the capability of the MHE of minimizing the noise while correlating a model with measurements is enriched with an l 1 -norm optimization in order to promote a sparse solution for the input estimation. A numerical example shows that the CS-MHE allows for an unknown input estimation in terms of magnitude, time and location, exploiting the assumption that the input is sparse in time and space. Finally, an experimental setup is presented as validation case.
Giuseppe Caire - One of the best experts on this subject based on the ideXlab platform.
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Joint State sensing and communication over memoryless multiple access channels
International Symposium on Information Theory, 2019Co-Authors: Mari Kobayashi, Hassan Hamad, Gerhard Kramer, Giuseppe CaireAbstract:A memoryless State-dependent multiple access channel (MAC) is considered where two transmitters wish to convey a respective message to a receiver while simultaneously estimating the respective channel State via generalized feedback. The scenario is motivated by a Joint radar and communication system where the radar and data applications share the same bandwidth. An achievable capacity-distortion tradeoff region is derived that outperforms a resource-sharing scheme through a binary erasure MAC with binary States.
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Joint State sensing and communication optimal tradeoff for a memoryless case
International Symposium on Information Theory, 2018Co-Authors: Mari Kobayashi, Giuseppe Caire, Gerhard KramerAbstract:Ahstract—A communication setup is considered where a transmitter wishes to simultaneously sense its channel State and convey a message to a receiver. The State is estimated at the transmitter by means of generalized feedback, i.e. a strictly causal channel output that is observed at the transmitter. The scenario is motivated by a Joint radar and communication system where the radar and data applications share the same frequency band. For the case of a memoryless channel with i.i.d. State sequences, we characterize the capacity-distortion tradeoff, defined as the best achievable rate below which a message can be conveyed reliably while satisfying some distortion constraint on State sensing. An iterative algorithm is proposed to optimize the input probability distribution. Examples demonstrate the benefits of Joint sensing and communication as compared to a separation-based approach.
Gerhard Kramer - One of the best experts on this subject based on the ideXlab platform.
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Joint State sensing and communication over memoryless multiple access channels
International Symposium on Information Theory, 2019Co-Authors: Mari Kobayashi, Hassan Hamad, Gerhard Kramer, Giuseppe CaireAbstract:A memoryless State-dependent multiple access channel (MAC) is considered where two transmitters wish to convey a respective message to a receiver while simultaneously estimating the respective channel State via generalized feedback. The scenario is motivated by a Joint radar and communication system where the radar and data applications share the same bandwidth. An achievable capacity-distortion tradeoff region is derived that outperforms a resource-sharing scheme through a binary erasure MAC with binary States.
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Joint State sensing and communication optimal tradeoff for a memoryless case
International Symposium on Information Theory, 2018Co-Authors: Mari Kobayashi, Giuseppe Caire, Gerhard KramerAbstract:Ahstract—A communication setup is considered where a transmitter wishes to simultaneously sense its channel State and convey a message to a receiver. The State is estimated at the transmitter by means of generalized feedback, i.e. a strictly causal channel output that is observed at the transmitter. The scenario is motivated by a Joint radar and communication system where the radar and data applications share the same frequency band. For the case of a memoryless channel with i.i.d. State sequences, we characterize the capacity-distortion tradeoff, defined as the best achievable rate below which a message can be conveyed reliably while satisfying some distortion constraint on State sensing. An iterative algorithm is proposed to optimize the input probability distribution. Examples demonstrate the benefits of Joint sensing and communication as compared to a separation-based approach.
Mari Kobayashi - One of the best experts on this subject based on the ideXlab platform.
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Joint State sensing and communication over memoryless multiple access channels
International Symposium on Information Theory, 2019Co-Authors: Mari Kobayashi, Hassan Hamad, Gerhard Kramer, Giuseppe CaireAbstract:A memoryless State-dependent multiple access channel (MAC) is considered where two transmitters wish to convey a respective message to a receiver while simultaneously estimating the respective channel State via generalized feedback. The scenario is motivated by a Joint radar and communication system where the radar and data applications share the same bandwidth. An achievable capacity-distortion tradeoff region is derived that outperforms a resource-sharing scheme through a binary erasure MAC with binary States.
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Joint State sensing and communication optimal tradeoff for a memoryless case
International Symposium on Information Theory, 2018Co-Authors: Mari Kobayashi, Giuseppe Caire, Gerhard KramerAbstract:Ahstract—A communication setup is considered where a transmitter wishes to simultaneously sense its channel State and convey a message to a receiver. The State is estimated at the transmitter by means of generalized feedback, i.e. a strictly causal channel output that is observed at the transmitter. The scenario is motivated by a Joint radar and communication system where the radar and data applications share the same frequency band. For the case of a memoryless channel with i.i.d. State sequences, we characterize the capacity-distortion tradeoff, defined as the best achievable rate below which a message can be conveyed reliably while satisfying some distortion constraint on State sensing. An iterative algorithm is proposed to optimize the input probability distribution. Examples demonstrate the benefits of Joint sensing and communication as compared to a separation-based approach.
Anatoly Zlotnik - One of the best experts on this subject based on the ideXlab platform.
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State and parameter estimation for natural gas pipeline networks using transient State data
IEEE Transactions on Control Systems and Technology, 2019Co-Authors: Kaarthik Sundar, Anatoly ZlotnikAbstract:We formulate two estimation problems for pipeline systems in which measurements of the compressible gas flowing through a network of pipes are affected by time-varying injections, withdrawals, and compression. We consider a State estimation problem that is then extended to a Joint State and parameter estimation problem that can be used for data assimilation. In both formulations, the flow dynamics are described on each pipe by space- and time-dependent densities and mass flux which evolve according to a system of coupled partial differential equations, in which momentum dissipation is modeled using the Darcy–Wiesbach friction approximation. These dynamics are first spatially discretized to obtain a system of nonlinear ordinary differential equations on which State and parameter estimation formulations are given as nonlinear least squares problems. A rapid, scalable computational method for performing a nonlinear least squares estimation is developed. Extensive simulations and computational experiments on multiple pipeline test networks demonstrate the effectiveness of the formulations in obtaining State and parameter estimates in the presence of measurement and process noise.
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State and parameter estimation for natural gas pipeline networks using transient State data
arXiv: Systems and Control, 2018Co-Authors: Kaarthik Sundar, Anatoly ZlotnikAbstract:We formulate two estimation problems for pipeline systems in which measurements of compressible gas flow through a network of pipes is affected by time-varying injections, withdrawals, and compression. We consider a State estimation problem that is then extended to a Joint State and parameter estimation problem that can be used for data assimilation. In both formulations, the flow dynamics are described on each pipe by space- and time-dependent density and mass flux that evolve according to a system of coupled partial differential equations, in which momentum dissipation is modelled using the Darcy-Wiesbach friction approximation. These dynamics are first spatially discretized to obtain a system of nonlinear ordinary differential equations on which State and parameter estimation formulations are given as nonlinear least squares problems. A rapid, scalable computational method for performing a nonlinear least squares estimation is developed. Extensive simulations and computational experiments on multiple pipeline test networks demonstrate the effectiveness of the formulations in obtaining State and parameter estimates in the presence of measurement and process noise.
