The Experts below are selected from a list of 4434 Experts worldwide ranked by ideXlab platform
A. Bouzerdoum - One of the best experts on this subject based on the ideXlab platform.
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A compressed sensing method for complex-valued signals with application to through-the-wall radar imaging
2013 IEEE International Conference on Acoustics Speech and Signal Processing, 2013Co-Authors: F. H. C. Tivive, A. BouzerdoumAbstract:In this paper, we present a compressed sensing method for complex-valued signals based on multiple Measurement Vector compressed sensing model. The proposed method constrains the real and imaginary parts of the recovered signal to have the same sparsity profile. It is applied to a compressed sensing through-the-wall radar imaging problem. Experiments based on synthetic data shows that the proposed method achieves lower reconstruction error than the existing CS method.
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Multiple-Measurement Vector model and its application to Through-the-Wall Radar Imaging
2011 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2011Co-Authors: J. Yang, A. Bouzerdoum, F. H. C. Tivive, M. G. AminAbstract:This paper addresses the problem of Through-the-Wall Radar Imaging (TWRI) using the Multiple-Measurement Vector (MMV) compressive sensing model. TWR image formation is reformulated as a compressed sensing (CS) problem, seeking a sparse representation in the spatial domain. In traditional CS-based through-the-wall radar imaging (TWRI) methods, the Measurement matrix is Vectorized so that a single Measurement Vector (SMV) model is applied to generate a sparse solution, which represents a scene comprising point-like targets. For multiple Measurement TWRI problems, the SMV model may produce a sub-optimum sparse solution. On the other hand, the proposed MMV model for TWRI generates a more sparse scene by processing all the Measurements simultaneously. To evaluate the effectiveness of the proposed method, it is applied to fuse multiple polarization data to form the radar image. Based on simulated data with different number of Measurements and noise levels, the proposed MMV-based TWRI method produces better TWR images in terms of image quality and detection accuracy.
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ICASSP - Multiple-Measurement Vector model and its application to Through-the-Wall Radar Imaging
2011 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2011Co-Authors: J. Yang, A. Bouzerdoum, F. H. C. Tivive, M. G. AminAbstract:This paper addresses the problem of Through-the-Wall Radar Imaging (TWRI) using the Multiple-Measurement Vector (MMV) compressive sensing model. TWR image formation is reformulated as a compressed sensing (CS) problem, seeking a sparse representation in the spatial domain. In traditional CS-based through-the-wall radar imaging (TWRI) methods, the Measurement matrix is Vectorized so that a single Measurement Vector (SMV) model is applied to generate a sparse solution, which represents a scene comprising point-like targets. For multiple Measurement TWRI problems, the SMV model may produce a sub-optimum sparse solution. On the other hand, the proposed MMV model for TWRI generates a more sparse scene by processing all the Measurements simultaneously. To evaluate the effectiveness of the proposed method, it is applied to fuse multiple polarization data to form the radar image. Based on simulated data with different number of Measurements and noise levels, the proposed MMV-based TWRI method produces better TWR images in terms of image quality and detection accuracy.
Jie Chen - One of the best experts on this subject based on the ideXlab platform.
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theoretical results on sparse representations of multiple Measurement Vectors
IEEE Transactions on Signal Processing, 2006Co-Authors: Jie Chen, Xiaoming HuoAbstract:The sparse representation of a multiple-Measurement Vector (MMV) is a relatively new problem in sparse representation. Efficient methods have been proposed. Although many theoretical results that are available in a simple case-single-Measurement Vector (SMV)-the theoretical analysis regarding MMV is lacking. In this paper, some known results of SMV are generalized to MMV. Some of these new results take advantages of additional information in the formulation of MMV. We consider the uniqueness under both an lscr0-norm-like criterion and an lscr1-norm-like criterion. The consequent equivalence between the lscr0-norm approach and the lscr1-norm approach indicates a computationally efficient way of finding the sparsest representation in a redundant dictionary. For greedy algorithms, it is proven that under certain conditions, orthogonal matching pursuit (OMP) can find the sparsest representation of an MMV with computational efficiency, just like in SMV. Simulations show that the predictions made by the proved theorems tend to be very conservative; this is consistent with some recent advances in probabilistic analysis based on random matrix theory. The connections will be discussed
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Sparse representations for multiple Measurement Vectors (MMV) in an over-complete dictionary
Proceedings. (ICASSP '05). IEEE International Conference on Acoustics Speech and Signal Processing 2005., 2005Co-Authors: Jie ChenAbstract:The multiple Measurement Vector (MMV), a newly emerged problem in sparse representation in an over-complete dictionary motivated by a neuro-magnetic inverse problem that arises in magnetoencephalography (MEG) - a modality for imaging the possible activation regions in the brain, poses new challenges. Efficient methods have been designed to search for sparse representations; however, we have not seen substantial development in the theoretical analysis, considering what has been done in a simpler case - single Measurement Vector (SMV) - in which many theoretical results are known. This paper extends the known results of SMV to MMV. Our theoretical results show the fundamental limitation on when a sparse representation is unique. Moreover, the relation between the solutions of /spl lscr//sub 0/-norm minimization and the solutions of /spl lscr//sub 1/-norm minimization indicates a computationally efficient approach to find a sparse representation. Interestingly, simulations show that the predictions made by these theorems tend to be conservative.
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ICASSP (4) - Sparse representations for multiple Measurement Vectors (MMV) in an over-complete dictionary
Proceedings. (ICASSP '05). IEEE International Conference on Acoustics Speech and Signal Processing 2005., 2005Co-Authors: Jie ChenAbstract:The multiple Measurement Vector (MMV), a newly emerged problem in sparse representation in an over-complete dictionary motivated by a neuro-magnetic inverse problem that arises in magnetoencephalography (MEG) - a modality for imaging the possible activation regions in the brain, poses new challenges. Efficient methods have been designed to search for sparse representations; however, we have not seen substantial development in the theoretical analysis, considering what has been done in a simpler case - single Measurement Vector (SMV) - in which many theoretical results are known. This paper extends the known results of SMV to MMV. Our theoretical results show the fundamental limitation on when a sparse representation is unique. Moreover, the relation between the solutions of /spl lscr//sub 0/-norm minimization and the solutions of /spl lscr//sub 1/-norm minimization indicates a computationally efficient approach to find a sparse representation. Interestingly, simulations show that the predictions made by these theorems tend to be conservative.
D. G. Walker - One of the best experts on this subject based on the ideXlab platform.
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high heat flux point source sensitivity and localization analysis for an ultrasonic sensor array
International Journal of Heat and Mass Transfer, 2012Co-Authors: M. R. Myers, A.b. Jorge, Márcia Justino Rossini Mutton, D. G. WalkerAbstract:Abstract State estimation procedures using the extended Kalman filter are investigated for a transient heat transfer problem in which a high heat flux point source is applied on one side of a thin plate and ultrasonic pulse time of flight is measured between spatially separated transducers on the opposite side of the plate. This work is an integral part of an effort to develop a system capable of locating the boundary layer transition region on a hypersonic vehicle aeroshell. Results from thermal conduction experiments involving one-way ultrasonic pulse time of flight Measurements are presented. Uncertainties in the experiments and sensitivity to heating source location are discussed. One key finding is that sensitivity to heating source location is greater in the direction normal to the ultrasonic pulse propagation path. Scaled sensitivities to boundary conditions and thermal conductivity are presented and analyzed for all possible source locations using a square sensor grid. While sensitivity to the primary heat flux was determined to be the highest, sensitivity to the other parameters is either on the same order of magnitude or one order of magnitude less. Two different Measurement models are compared for heating source localization: (1) directly using the one-way ultrasonic pulse time of flight as the Measurement Vector and (2) indirectly obtaining distance from the one-way ultrasonic pulse time of flight and then using these obtained distances as the Measurement Vector in the extended Kalman filter. Heating source localization results and convergence behavior are compared for the two Measurement models. Two areas of sensitivity analyses are presented: (1) heat source location relative to sensor array position, and (2) sensor noise. The direct Measurement model produced the best results when considering accuracy of converged solution, ability to converge to the correct solution given different initial guesses, and smoothness of convergence behavior.
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High heat flux point source sensitivity and localization analysis for an ultrasonic sensor array
International Journal of Heat and Mass Transfer, 2012Co-Authors: M. R. Myers, A.b. Jorge, Márcia Justino Rossini Mutton, D. G. WalkerAbstract:State estimation procedures using the extended Kalman filter are investigated for a transient heat transfer problem in which a high heat flux point source is applied on one side of a thin plate and ultrasonic pulse time of flight is measured between spatially separated transducers on the opposite side of the plate. This work is an integral part of an effort to develop a system capable of locating the boundary layer transition region on a hypersonic vehicle aeroshell. Results from thermal conduction experiments involving one-way ultrasonic pulse time of flight Measurements are presented. Uncertainties in the experiments and sensitivity to heating source location are discussed. One key finding is that sensitivity to heating source location is greater in the direction normal to the ultrasonic pulse propagation path. Scaled sensitivities to boundary conditions and thermal conductivity are presented and analyzed for all possible source locations using a square sensor grid. While sensitivity to the primary heat flux was determined to be the highest, sensitivity to the other parameters is either on the same order of magnitude or one order of magnitude less. Two different Measurement models are compared for heating source localization: (1) directly using the one-way ultrasonic pulse time of flight as the Measurement Vector and (2) indirectly obtaining distance from the one-way ultrasonic pulse time of flight and then using these obtained distances as the Measurement Vector in the extended Kalman filter. Heating source localization results and convergence behavior are compared for the two Measurement models. Two areas of sensitivity analyses are presented: (1) heat source location relative to sensor array position, and (2) sensor noise. The direct Measurement model produced the best results when considering accuracy of converged solution, ability to converge to the correct solution given different initial guesses, and smoothness of convergence behavior. © 2012 Elsevier Ltd. All rights reserved.
F. H. C. Tivive - One of the best experts on this subject based on the ideXlab platform.
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A compressed sensing method for complex-valued signals with application to through-the-wall radar imaging
2013 IEEE International Conference on Acoustics Speech and Signal Processing, 2013Co-Authors: F. H. C. Tivive, A. BouzerdoumAbstract:In this paper, we present a compressed sensing method for complex-valued signals based on multiple Measurement Vector compressed sensing model. The proposed method constrains the real and imaginary parts of the recovered signal to have the same sparsity profile. It is applied to a compressed sensing through-the-wall radar imaging problem. Experiments based on synthetic data shows that the proposed method achieves lower reconstruction error than the existing CS method.
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Multiple-Measurement Vector model and its application to Through-the-Wall Radar Imaging
2011 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2011Co-Authors: J. Yang, A. Bouzerdoum, F. H. C. Tivive, M. G. AminAbstract:This paper addresses the problem of Through-the-Wall Radar Imaging (TWRI) using the Multiple-Measurement Vector (MMV) compressive sensing model. TWR image formation is reformulated as a compressed sensing (CS) problem, seeking a sparse representation in the spatial domain. In traditional CS-based through-the-wall radar imaging (TWRI) methods, the Measurement matrix is Vectorized so that a single Measurement Vector (SMV) model is applied to generate a sparse solution, which represents a scene comprising point-like targets. For multiple Measurement TWRI problems, the SMV model may produce a sub-optimum sparse solution. On the other hand, the proposed MMV model for TWRI generates a more sparse scene by processing all the Measurements simultaneously. To evaluate the effectiveness of the proposed method, it is applied to fuse multiple polarization data to form the radar image. Based on simulated data with different number of Measurements and noise levels, the proposed MMV-based TWRI method produces better TWR images in terms of image quality and detection accuracy.
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ICASSP - Multiple-Measurement Vector model and its application to Through-the-Wall Radar Imaging
2011 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2011Co-Authors: J. Yang, A. Bouzerdoum, F. H. C. Tivive, M. G. AminAbstract:This paper addresses the problem of Through-the-Wall Radar Imaging (TWRI) using the Multiple-Measurement Vector (MMV) compressive sensing model. TWR image formation is reformulated as a compressed sensing (CS) problem, seeking a sparse representation in the spatial domain. In traditional CS-based through-the-wall radar imaging (TWRI) methods, the Measurement matrix is Vectorized so that a single Measurement Vector (SMV) model is applied to generate a sparse solution, which represents a scene comprising point-like targets. For multiple Measurement TWRI problems, the SMV model may produce a sub-optimum sparse solution. On the other hand, the proposed MMV model for TWRI generates a more sparse scene by processing all the Measurements simultaneously. To evaluate the effectiveness of the proposed method, it is applied to fuse multiple polarization data to form the radar image. Based on simulated data with different number of Measurements and noise levels, the proposed MMV-based TWRI method produces better TWR images in terms of image quality and detection accuracy.
M. G. Amin - One of the best experts on this subject based on the ideXlab platform.
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Multiple-Measurement Vector model and its application to Through-the-Wall Radar Imaging
2011 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2011Co-Authors: J. Yang, A. Bouzerdoum, F. H. C. Tivive, M. G. AminAbstract:This paper addresses the problem of Through-the-Wall Radar Imaging (TWRI) using the Multiple-Measurement Vector (MMV) compressive sensing model. TWR image formation is reformulated as a compressed sensing (CS) problem, seeking a sparse representation in the spatial domain. In traditional CS-based through-the-wall radar imaging (TWRI) methods, the Measurement matrix is Vectorized so that a single Measurement Vector (SMV) model is applied to generate a sparse solution, which represents a scene comprising point-like targets. For multiple Measurement TWRI problems, the SMV model may produce a sub-optimum sparse solution. On the other hand, the proposed MMV model for TWRI generates a more sparse scene by processing all the Measurements simultaneously. To evaluate the effectiveness of the proposed method, it is applied to fuse multiple polarization data to form the radar image. Based on simulated data with different number of Measurements and noise levels, the proposed MMV-based TWRI method produces better TWR images in terms of image quality and detection accuracy.
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ICASSP - Multiple-Measurement Vector model and its application to Through-the-Wall Radar Imaging
2011 IEEE International Conference on Acoustics Speech and Signal Processing (ICASSP), 2011Co-Authors: J. Yang, A. Bouzerdoum, F. H. C. Tivive, M. G. AminAbstract:This paper addresses the problem of Through-the-Wall Radar Imaging (TWRI) using the Multiple-Measurement Vector (MMV) compressive sensing model. TWR image formation is reformulated as a compressed sensing (CS) problem, seeking a sparse representation in the spatial domain. In traditional CS-based through-the-wall radar imaging (TWRI) methods, the Measurement matrix is Vectorized so that a single Measurement Vector (SMV) model is applied to generate a sparse solution, which represents a scene comprising point-like targets. For multiple Measurement TWRI problems, the SMV model may produce a sub-optimum sparse solution. On the other hand, the proposed MMV model for TWRI generates a more sparse scene by processing all the Measurements simultaneously. To evaluate the effectiveness of the proposed method, it is applied to fuse multiple polarization data to form the radar image. Based on simulated data with different number of Measurements and noise levels, the proposed MMV-based TWRI method produces better TWR images in terms of image quality and detection accuracy.