2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2019
The optimal robust adaptive beamforming problem based on worst-case signal-to-noise-plus-interfer... more The optimal robust adaptive beamforming problem based on worst-case signal-to-noise-plus-interference ratio (SINR) maximization with a nonconvex uncertainty set of the desired steering vectors is considered. The uncertainty set consists of a similarity constraint and a (nonconvex) double-sided ball constraint. The worst-case SINR maximization problem is turned into a quadratic matrix inequality (QMI) problem using the strong duality of semidefinite programs. Then the linear matrix inequality (LMI) relaxation for the QMI problem is formulated, and is further restricted by adding an equivalent representation for the second largest eigenvalue of the positive semidefinite beamforming matrix to be nonnegative. It turns out that the restricted LMI problem is a bilinear matrix inequality (BLMI) relaxation problem. We propose an iterative algorithm to solve the BLMI problem that finds an optimal/suboptimal solution for the original QMI problem for the worst-case SINR maximization problem. To validate our results, simulation examples are presented and demonstrate the improved performance of the proposed robust beamformer in terms of the array output SINR.
ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020
With a change of signal notion to graph signal, new means of performing blind source separation (... more With a change of signal notion to graph signal, new means of performing blind source separation (BSS) appear. Particularly, existing independent component analysis (ICA) methods exploit the non-Gaussianity of the signals or other types of prior information. For graph signals, such prior information is present in a graph of dependencies in the signals. We propose BSS of graph signals which uses the prior information presented by the signal graph together with nonGaussianity. We derive the identifiability conditions for the proposed method and compare them to the conditions when only graph or non-Gaussianity approach is used. In simulation studies, we verify that the new method can separate a broader range of graph signals and show that it is also more efficient when both approaches are useful.
2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2015
In this paper, a simple but efficient permutation enhanced parallel reconstruction architecture f... more In this paper, a simple but efficient permutation enhanced parallel reconstruction architecture for compressive sampling (CS) is proposed. In this architecture, a measurement matrix is constructed from a block-diagonal sensing matrix, the sparsifying basis of the target signal, and a pre-defined permutation matrix. In this way, the projection of the signal onto the sparsifying basis can be divided into several segments and all segments can be reconstructed in parallel. Thus, the computational complexity and the time for reconstruction can be reduced significantly. With a good permutation matrix, the error performance of the proposed method can be improved compared with the option without permutation. The proposed method can be used in applications where the computational complexity and time for reconstruction are crucial evaluation criteria and centralized sampling is acceptable. Simulation results show that the proposed method can achieve comparable results to the centralized recon...
2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2015
The waveform separation based on matched filtering leads to the cross correlation interference, w... more The waveform separation based on matched filtering leads to the cross correlation interference, which deteriorates the performance of multiple-input multiple-output (MIMO) radar system. This paper investigates the performance of a waveform separation approach employing a novel orthogonal frequency division multiplexing scheme for MIMO synthetic aperture radar. The approach enables to separate the waveforms perfectly even though the waveforms are on common spectral support. By means of theoretical analysis confirmed also by simulations, we show that the proposed scheme decreases sidelobe ratio. Thus, the high-quality imaging can be achieved.
2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2015
This paper presents a new approach to solving the rank constrained beamforming problem. Instead o... more This paper presents a new approach to solving the rank constrained beamforming problem. Instead of relaxing the problem to a feasible set of the positive semidefinite matrices, we restrict the problem to a space of polynomials whose dimension is equal to the desired rank. The solution to the resulting optimization is then required to be full rank, allowing a simple matrix decomposition to recover the beamforming matrix exactly. Simulation results show an exact agreement of the solution with the proposed algebraic structure, as well as significant performance benefits in terms of sidelobe suppression compared with previous methods.
Simultaneous sparse approximation (SSA) seeks to represent a set of dependent signals using spars... more Simultaneous sparse approximation (SSA) seeks to represent a set of dependent signals using sparse vectors with identical supports. The SSA model has been used in various signal and image processing applications involving multiple correlated input signals. In this paper, we propose algorithms for convolutional SSA (CSSA) based on the alternating direction method of multipliers. Specifically, we address the CSSA problem with different sparsity structures and the convolutional feature learning problem in multimodal data/signals based on the SSA model. We evaluate the proposed algorithms by applying them to multimodal and multifocus image fusion problems.
Sparse array (SA) geometries, such as coprime and nested arrays, can be regarded as a concatenati... more Sparse array (SA) geometries, such as coprime and nested arrays, can be regarded as a concatenation of two uniform linear arrays (ULAs). Such arrays lead to a significant increase of the number of degrees of freedom (DOF) when the second-order information is utilized, i.e., they provide long virtual difference coarray (DCA). Thus, the idea of this paper is based on the observation that SAs can be fitted through concatenation of sub-ULAs. A corresponding SA design principle, called ULA fitting, is then proposed. It aims to design SAs from sub-ULAs. Towards this goal, a polynomial model for arrays is used, and based on it, a DCA structure is analyzed if SA is composed of multiple sub-ULAs. SA design with low mutual coupling is considered. ULA fitting enables to transfer the SA design requirements, such as hole free, low mutual coupling and other requirements, into pseudo polynomial equation, and hence, find particular solutions. We mainly focus on designing SAs with low mutual couplin...
In this paper, we propose a transmit beamspace energy focusing technique for multiple-input multi... more In this paper, we propose a transmit beamspace energy focusing technique for multiple-input multiple-output (MIMO) radar with application to direction finding for multiple targets. The general angular directions of the targets are assumed to be located within a certain spatial sector. We focus the energy of multiple (two or more) transmitted orthogonal waveforms within that spatial sector using transmit beamformers which are designed to improve the signal-to-noise ratio (SNR) gain at each receive antenna. The subspace decomposition-based techniques such as MUSIC can then be used for direction finding for multiple targets. Moreover, the transmit beamformers can be designed so that matched-filtering the received data to the waveforms yields multiple (two or more) data sets with rotational invariance property that allows applying search-free direction finding techniques such as ESPRIT for two data sets or parallel factor analysis (PARAFAC) for more than two data sets. Unlike previously...
In this paper, we address the problem of transmit beamspace design for multiple-input multiple-ou... more In this paper, we address the problem of transmit beamspace design for multiple-input multiple-output (MIMO) radar with colocated antennas in application to direction-of-arrival (DOA) estimation. A new method for designing the transmit beamspace matrix that enables the use of search-free DOA estimation techniques at the receiver is introduced. The essence of the proposed method is to design the transmit beamspace matrix based on minimizing the difference between a desired transmit beampattern and the actual one under the constraint of uniform power distribution across the transmit array elements. The desired transmit beampattern can be of arbitrary shape and is allowed to consist of one or more spatial sectors. The number of transmit waveforms is even but otherwise arbitrary. To allow for simple search-free DOA estimation algorithms at the receive array, the rotational invariance property is established at the transmit array by imposing a specific structure on the beamspace matrix. ...
2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2019
The optimal robust adaptive beamforming problem based on worst-case signal-to-noise-plus-interfer... more The optimal robust adaptive beamforming problem based on worst-case signal-to-noise-plus-interference ratio (SINR) maximization with a nonconvex uncertainty set of the desired steering vectors is considered. The uncertainty set consists of a similarity constraint and a (nonconvex) double-sided ball constraint. The worst-case SINR maximization problem is turned into a quadratic matrix inequality (QMI) problem using the strong duality of semidefinite programs. Then the linear matrix inequality (LMI) relaxation for the QMI problem is formulated, and is further restricted by adding an equivalent representation for the second largest eigenvalue of the positive semidefinite beamforming matrix to be nonnegative. It turns out that the restricted LMI problem is a bilinear matrix inequality (BLMI) relaxation problem. We propose an iterative algorithm to solve the BLMI problem that finds an optimal/suboptimal solution for the original QMI problem for the worst-case SINR maximization problem. To validate our results, simulation examples are presented and demonstrate the improved performance of the proposed robust beamformer in terms of the array output SINR.
ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2020
With a change of signal notion to graph signal, new means of performing blind source separation (... more With a change of signal notion to graph signal, new means of performing blind source separation (BSS) appear. Particularly, existing independent component analysis (ICA) methods exploit the non-Gaussianity of the signals or other types of prior information. For graph signals, such prior information is present in a graph of dependencies in the signals. We propose BSS of graph signals which uses the prior information presented by the signal graph together with nonGaussianity. We derive the identifiability conditions for the proposed method and compare them to the conditions when only graph or non-Gaussianity approach is used. In simulation studies, we verify that the new method can separate a broader range of graph signals and show that it is also more efficient when both approaches are useful.
2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2015
In this paper, a simple but efficient permutation enhanced parallel reconstruction architecture f... more In this paper, a simple but efficient permutation enhanced parallel reconstruction architecture for compressive sampling (CS) is proposed. In this architecture, a measurement matrix is constructed from a block-diagonal sensing matrix, the sparsifying basis of the target signal, and a pre-defined permutation matrix. In this way, the projection of the signal onto the sparsifying basis can be divided into several segments and all segments can be reconstructed in parallel. Thus, the computational complexity and the time for reconstruction can be reduced significantly. With a good permutation matrix, the error performance of the proposed method can be improved compared with the option without permutation. The proposed method can be used in applications where the computational complexity and time for reconstruction are crucial evaluation criteria and centralized sampling is acceptable. Simulation results show that the proposed method can achieve comparable results to the centralized recon...
2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2015
The waveform separation based on matched filtering leads to the cross correlation interference, w... more The waveform separation based on matched filtering leads to the cross correlation interference, which deteriorates the performance of multiple-input multiple-output (MIMO) radar system. This paper investigates the performance of a waveform separation approach employing a novel orthogonal frequency division multiplexing scheme for MIMO synthetic aperture radar. The approach enables to separate the waveforms perfectly even though the waveforms are on common spectral support. By means of theoretical analysis confirmed also by simulations, we show that the proposed scheme decreases sidelobe ratio. Thus, the high-quality imaging can be achieved.
2015 IEEE 6th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2015
This paper presents a new approach to solving the rank constrained beamforming problem. Instead o... more This paper presents a new approach to solving the rank constrained beamforming problem. Instead of relaxing the problem to a feasible set of the positive semidefinite matrices, we restrict the problem to a space of polynomials whose dimension is equal to the desired rank. The solution to the resulting optimization is then required to be full rank, allowing a simple matrix decomposition to recover the beamforming matrix exactly. Simulation results show an exact agreement of the solution with the proposed algebraic structure, as well as significant performance benefits in terms of sidelobe suppression compared with previous methods.
Simultaneous sparse approximation (SSA) seeks to represent a set of dependent signals using spars... more Simultaneous sparse approximation (SSA) seeks to represent a set of dependent signals using sparse vectors with identical supports. The SSA model has been used in various signal and image processing applications involving multiple correlated input signals. In this paper, we propose algorithms for convolutional SSA (CSSA) based on the alternating direction method of multipliers. Specifically, we address the CSSA problem with different sparsity structures and the convolutional feature learning problem in multimodal data/signals based on the SSA model. We evaluate the proposed algorithms by applying them to multimodal and multifocus image fusion problems.
Sparse array (SA) geometries, such as coprime and nested arrays, can be regarded as a concatenati... more Sparse array (SA) geometries, such as coprime and nested arrays, can be regarded as a concatenation of two uniform linear arrays (ULAs). Such arrays lead to a significant increase of the number of degrees of freedom (DOF) when the second-order information is utilized, i.e., they provide long virtual difference coarray (DCA). Thus, the idea of this paper is based on the observation that SAs can be fitted through concatenation of sub-ULAs. A corresponding SA design principle, called ULA fitting, is then proposed. It aims to design SAs from sub-ULAs. Towards this goal, a polynomial model for arrays is used, and based on it, a DCA structure is analyzed if SA is composed of multiple sub-ULAs. SA design with low mutual coupling is considered. ULA fitting enables to transfer the SA design requirements, such as hole free, low mutual coupling and other requirements, into pseudo polynomial equation, and hence, find particular solutions. We mainly focus on designing SAs with low mutual couplin...
In this paper, we propose a transmit beamspace energy focusing technique for multiple-input multi... more In this paper, we propose a transmit beamspace energy focusing technique for multiple-input multiple-output (MIMO) radar with application to direction finding for multiple targets. The general angular directions of the targets are assumed to be located within a certain spatial sector. We focus the energy of multiple (two or more) transmitted orthogonal waveforms within that spatial sector using transmit beamformers which are designed to improve the signal-to-noise ratio (SNR) gain at each receive antenna. The subspace decomposition-based techniques such as MUSIC can then be used for direction finding for multiple targets. Moreover, the transmit beamformers can be designed so that matched-filtering the received data to the waveforms yields multiple (two or more) data sets with rotational invariance property that allows applying search-free direction finding techniques such as ESPRIT for two data sets or parallel factor analysis (PARAFAC) for more than two data sets. Unlike previously...
In this paper, we address the problem of transmit beamspace design for multiple-input multiple-ou... more In this paper, we address the problem of transmit beamspace design for multiple-input multiple-output (MIMO) radar with colocated antennas in application to direction-of-arrival (DOA) estimation. A new method for designing the transmit beamspace matrix that enables the use of search-free DOA estimation techniques at the receiver is introduced. The essence of the proposed method is to design the transmit beamspace matrix based on minimizing the difference between a desired transmit beampattern and the actual one under the constraint of uniform power distribution across the transmit array elements. The desired transmit beampattern can be of arbitrary shape and is allowed to consist of one or more spatial sectors. The number of transmit waveforms is even but otherwise arbitrary. To allow for simple search-free DOA estimation algorithms at the receive array, the rotational invariance property is established at the transmit array by imposing a specific structure on the beamspace matrix. ...
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Papers by Sergiy A . Vorobyov