The Experts below are selected from a list of 546750 Experts worldwide ranked by ideXlab platform
Yuriy Shkvarko - One of the best experts on this subject based on the ideXlab platform.
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dynamic Experiment Design regularization approach to adaptive imaging with array radar sar sensor systems
Sensors, 2011Co-Authors: Yuriy Shkvarko, Jose Tuxpan, Stewart SantosAbstract:We consider a problem of high-resolution array radar/SAR imaging formalized in terms of a nonlinear ill-posed inverse problem of nonparametric estimation of the power spatial spectrum pattern (SSP) of the random wavefield scattered from a remotely sensed scene observed through a kernel signal formation operator and contaminated with random Gaussian noise. First, the Sobolev-type solution space is constructed to specify the class of consistent kernel SSP estimators with the reproducing kernel structures adapted to the metrics in such the solution space. Next, the “model-free” variational analysis (VA)-based image enhancement approach and the “model-based” descriptive Experiment Design (DEED) regularization paradigm are unified into a new dynamic Experiment Design (DYED) regularization framework. Application of the proposed DYED framework to the adaptive array radar/SAR imaging problem leads to a class of two-level (DEED-VA) regularized SSP reconstruction techniques that aggregate the kernel adaptive anisotropic windowing with the projections onto convex sets to enforce the consistency and robustness of the overall iterative SSP estimators. We also show how the proposed DYED regularization method may be considered as a generalization of the MVDR, APES and other high-resolution nonparametric adaptive radar sensing techniques. A family of the DYED-related algorithms is constructed and their effectiveness is finally illustrated via numerical simulations.
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Experiment Design regularization based hardware software coDesign for real time enhanced imaging in uncertain remote sensing environment
EURASIP Journal on Advances in Signal Processing, 2010Co-Authors: Castillo A Atoche, Torres D Roman, Yuriy ShkvarkoAbstract:A new aggregated Hardware/Software (HW/SW) coDesign approach to optimization of the digital signal processing techniques for enhanced imaging with real-world uncertain remote sensing (RS) data based on the concept of descriptive Experiment Design regularization (DEDR) is addressed. We consider the applications of the developed approach to typical single-look synthetic aperture radar (SAR) imaging systems operating in the real-world uncertain RS scenarios. The software Design is aimed at the algorithmic-level decrease of the computational load of the large-scale SAR image enhancement tasks. The innovative algorithmic idea is to incorporate into the DEDR-optimized fixed-point iterative reconstruction/enhancement procedure the convex convergence enforcement regularization via constructing the proper multilevel projections onto convex sets (POCS) in the solution domain. The hardware Design is performed via systolic array computing based on a Xilinx Field Programmable Gate Array (FPGA) XC4VSX35-10ff668 and is aimed at implementing the unified DEDR-POCS image enhancement/reconstruction procedures in a computationally efficient multi-level parallel fashion that meets the (near) real-time image processing requirements. Finally, we comment on the simulation results indicative of the significantly increased performance efficiency both in resolution enhancement and in computational complexity reduction metrics gained with the proposed aggregated HW/SW co-Design approach.
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unifying Experiment Design and convex regularization techniques for enhanced imaging with uncertain remote sensing data part i theory
IEEE Transactions on Geoscience and Remote Sensing, 2010Co-Authors: Yuriy ShkvarkoAbstract:This paper considers the problem of high-resolution remote sensing (RS) of the environment formalized in the terms of a nonlinear ill-posed inverse problem of estimation of the power spatial spectrum pattern (SSP) of the wavefield scattered from an extended remotely sensed scene via processing the discrete measurements of a finite number of independent realizations of the observed degraded data signals [single realization of the trajectory signal in the case of synthetic aperture radar (SAR)]. We address a new descriptive Experiment Design regularization (DEDR) approach to treat the SSP reconstruction problem in the uncertain RS environment that unifies the paradigms of maximum likelihood nonparametric spectral estimation, descriptive Experiment Design, and worst case statistical performance optimization-based regularization. Pursuing such an approach, we establish a family of the DEDR-related SSP estimators that encompass a manifold of algorithms ranging from the traditional matched filter to the modified robust adaptive spatial filtering and minimum variance beamforming methods. The theoretical study is resumed with the development of a fixed-point iterative DEDR technique that incorporates the regularizing projections onto convex solution sets into the SSP reconstruction procedures to enforce the robustness and convergence. For the imaging SAR application, the proposed DEDR approach is aimed at performing, in a single optimized processing, adaptive SAR focusing, speckle reduction and RS scene image enhancement, and accounts for the possible presence of uncertain trajectory deviations.
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enhanced radar imaging in uncertain environment a descriptive Experiment Design regularization approach
International Journal of Navigation and Observation, 2008Co-Authors: Yuriy Shkvarko, Hector Perezmeana, Alejandro CastilloatocheAbstract:A new robust technique for high-resolution reconstructive imaging is developed as required for enhanced remote sensing (RS) with imaging array radar or/and synthetic aperture radar (SAR) operating in an uncertain RS environment. The operational scenario uncertainties are associated with the unknown statistics of perturbations of the signal formation operator (SFO) in turbulent medium, imperfect array calibration, finite dimensionality of measurements, uncontrolled antenna vibrations, and random carrier trajectory deviations in the case of SAR. We propose new descriptive Experiment Design regularization (DEDR) approach to treat the uncertain radar image enhancement/reconstruction problems. The proposed DEDR incorporates into the minimum risk (MR) nonparametric estimation strategy the Experiment Design-motivated operational constraints algorithmically coupled with the worst-case statistical performance (WCSP) optimization-based regularization. The MR objective functional is constrained by the WCSP information, and the robust DEDR image reconstruction operator applicable to the scenarios with the low-rank uncertain estimated data correlation matrices is found. We report and discuss some simulation results related to enhancement of the uncertain SAR imagery indicative of the significantly increased performance efficiency gained with the developed approach.
Hakan Hjalmarsson - One of the best experts on this subject based on the ideXlab platform.
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Application-Oriented Least Squares Experiment Design in Multicarrier Communication Systems ⋆
2016Co-Authors: Dimitrios Katselis, Cristian R Rojas, Hakan HjalmarssonAbstract:Abstract: Recently, the application-oriented framework for pilot Design in communication systems has been introduced. This framework is mostly appropriate for such a Design since the training sequences are selected to optimize a final performance metric of interest and not some of the classical metrics quantifying the distance between the estimated model and the true one, e.g., the mean square error (MSE). In this perspective, the known pilot sequences that are optimal for any communication system and for any estimation task have to be reexamined. In this paper, the problem of training pilot Design for the task of channel estimation in cyclic prefixed orthogonal frequency division multiplexing (CP-OFDM) systems is revisited. So far, the optimal training sequences for least squares (LS) channel estimation with respect to minimizing the channel MSE under a training energy constraint have been derived. Here, we investigate the same problem for the LS channel estimator, but when the Design takes into account an end performance metric of interest, namely, the symbol estimate MSE. Based on some convex approximations, we verify that the optimal full preamble, i.e, the preamble employing pilots on all subcarriers, for LS channel estimation in its classical context are near optimal in the aforementioned application-oriented context for the symbol estimate MSE in certain target signal-to-noise ratio (SNR) operating intervals. 1
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vector dither Experiment Design and direct parametric identification of reversed field pinch normal modes
Conference on Decision and Control, 2009Co-Authors: Erik Olofsson, Hakan Hjalmarsson, Cristian R Rojas, Per Brunsell, James R DrakeAbstract:Magnetic confinement fusion (MCF) research ambitiously endeavours to develop a major future energy source. MCF power plant Designs, typically some variation on the tokamak, unfortunately suffer from magnetohydrodynamic (MHD) instabilities. One unstable mode is known as the resistive-wall mode (RWM) which is a macroscopically global type of perturbation that can degrade or even terminate the plasma in the reactor if not stabilized. In this work the topic of RWMs is studied for the reversed-field pinch (RFP), another toroidal MCF concept, similar to the tokamak. The problem of identifying RWM dynamics during closed-loop operation is tackled by letting physics-based parametric modeling join forces with convex programming Experiment Design. An established MHD normal modes description is assessed for the RFP by synthesizing a multivariable dither signal where spatial fourier modes are spectrally shaped, with regard to real Experiment constraints, to yield minimum variance parameter estimates in the prediction-error framework. The dithering is applied to the real RFP plant EXTRAP-T2R, and Experimental MHD spectra are obtained by an automated procedure.
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identification for control of multivariable systems controller validation and Experiment Design via lmis
Automatica, 2008Co-Authors: Marta Barenthin, Hakan Hjalmarsson, Xavier Bombois, Gerard ScorlettiAbstract:This paper presents a new controller validation method for linear multivariable time-invariant models. Classical prediction error system identification methods deliver uncertainty regions which are nonstandard in the robust control literature. Our controller validation criterion computes an upper bound for the worst case performance, measured in terms of the H"~-norm of a weighted closed loop transfer matrix, achieved by a given controller over all plants in such uncertainty sets. This upper bound on the worst case performance is computed via an LMI-based optimization problem and is deduced via the separation of graph framework. Our main technical contribution is to derive, within that framework, a very general parametrization for the set of multipliers corresponding to the nonstandard uncertainty regions resulting from PE identification of MIMO systems. The proposed approach also allows for iterative Experiment Design. The results of this paper are asymptotic in the data length and it is assumed that the model structure is flexible enough to capture the true system.
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closed loop Experiment Design for linear time invariant dynamical systems via lmis
Automatica, 2008Co-Authors: Hakan Hjalmarsson, Henrik JanssonAbstract:All stationary Experimental conditions corresponding to a discrete-time linear time-invariant causal internally stable closed loop with real rational system and feedback controller are characterized using the Youla-Kucera parametrization. Finite dimensional parametrizations of the input spectrum and the Youla-Kucera parameter allow a wide range of closed loop Experiment Design problems, based on the asymptotic (in the sample size) covariance matrix for the estimated parameters, to be recast as computationally tractable convex optimization problems such as semi-definite programs. In particular, for Box-Jenkins models, a finite dimensional parametrization is provided which is able to generate all possible asymptotic covariance matrices. As a special case, the very common situation of a fixed controller during the identification Experiment can be handled and optimal reference signal spectra can be computed subject to closed loop signal constraints. Finally, a brief numerical comparison with closed loop Experiment Design based on a high model order variance expression is presented.
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from Experiment Design to closed loop control
Automatica, 2005Co-Authors: Hakan HjalmarssonAbstract:The links between identification and control are examined. The main trends in this research area are summarized, with particular focus on the Design of low complexity controllers from a statistical perspective. It is argued that a guiding principle should be to model as well as possible before any model or controller simplifications are made as this ensures the best statistical accuracy. This does not necessarily mean that a full-order model always is necessary as well Designed Experiments allow for restricted complexity models to be near-optimal. Experiment Design can therefore be seen as the key to successful applications. For this reason, particular attention is given to the interaction between Experimental constraints and performance specifications.
Daniel C Alexander - One of the best experts on this subject based on the ideXlab platform.
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a general framework for Experiment Design in diffusion mri and its application in measuring direct tissue microstructure features
Magnetic Resonance in Medicine, 2008Co-Authors: Daniel C AlexanderAbstract:This article introduces a new and general framework for optimizing the Experiment Design for diffusion MRI of samples with unknown orientation. An illustration then uses the framework to study the feasibility of measuring direct features of brain-tissue microstructure in vivo. The study investigates the accuracy and precision with which we can estimate potentially important new biomarkers such as axon density and radius in white matter. Simulation Experiments use a simple model of white matter based on CHARMED (composite hindered and restricted model of diffusion). The optimization finds acquisition protocols achievable on modern human and animal systems that consist of 120 measurements with fixed maximum gradient strengths. Axon radii in brain tissue are typically in the range 0.25–10 µm. Simulations suggest that estimates of radii in the range 5–10 µm have highest precision and that a maximum gradient strength of 0.07 T m−1 is sufficient to distinguish radii of 5, 10, and 20 µm. Smaller radii are more difficult to distinguish from one another but are identifiable as small. A maximum gradient strength of 0.2 Tm −1 distinguishes radii of 1 and 2 µm. The simulations also suggest that axon densities and diffusivity parameters in the normal range for white matter are recoverable. The Experiment-Design optimization has applications well beyond the current work to optimize the protocol for fitting any model of the diffusion process. Magn Reson Med 60:439–448, 2008. © 2008 Wiley-Liss, Inc.
Dc Alexander - One of the best experts on this subject based on the ideXlab platform.
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A general framework for Experiment Design in diffusion MRI and its application in measuring direct tissue-microstructure features
Magnetic Resonance in Medicine, 2008Co-Authors: Dc AlexanderAbstract:This article introduces a new and general framework for optimizing the Experiment Design for diffusion MRI of samples with unknown orientation. An illustration then uses the framework to study the feasibility of measuring direct features of brain-tissue microstructure in vivo. The study investigates the accuracy and precision with which we can estimate potentially important new biomarkers such as axon density and radius in white matter. Simulation Experiments use a simple model of white matter based on CHARMED (composite hindered and restricted model of diffusion). The optimization finds acquisition protocols achievable on modern human and animal systems that consist of 120 measurements with fixed maximum gradient strengths. Axon radii in brain tissue are typically in the range 0.25-10 mu m. Simulations suggest that estimates of radii in the range 5-10 mu m have highest precision and that a maximum gradient strength of 0.07 T m(-1) is sufficient to distinguish radii of 5, 10, and 20 pm. Smaller radii are more difficult to distinguish from one another but are identifiable as small. A maximum gradient strength of 0.2 T m(-1) distinguishes radii of 1 and 2 mu m. The simulations also suggest that axon densities and diffusivity parameters in the normal range for white matter are recoverable. The Experiment-Design optimization has applications well beyond the current work to optimize the protocol for fitting any model of the diffusion process.
Henk J Van Waarde - One of the best experts on this subject based on the ideXlab platform.
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beyond persistent excitation online Experiment Design for data driven modeling and control
IEEE Control Systems Letters, 2022Co-Authors: Henk J Van WaardeAbstract:This letter presents a new Experiment Design method for data-driven modeling and control. The idea is to select inputs online (using past input/output data), leading to desirable rank properties of data Hankel matrices. In comparison to the classical persistency of excitation condition, this online approach requires less data samples and is even shown to be completely sample efficient.
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beyond persistent excitation online Experiment Design for data driven modeling and control
arXiv: Optimization and Control, 2021Co-Authors: Henk J Van WaardeAbstract:This paper presents a new Experiment Design method for data-driven modeling and control. The idea is to select inputs online (using past input/output data), leading to desirable rank properties of data Hankel matrices. In comparison to the classical persistency of excitation condition, this online approach requires less data samples and is even shown to be completely sample efficient.
