The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform

Zhiquan Luo - One of the best experts on this subject based on the ideXlab platform.

  • worst case sinr maximization based robust Adaptive Beamforming problem with a nonconvex uncertainty set
    IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2019
    Co-Authors: Yongwei Huang, Sergiy A. Vorobyov, Zhiquan Luo
    Abstract:

    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.

  • Adaptive Beamforming with joint robustness against mismatched signal steering vector and interference nonstationarity
    IEEE Signal Processing Letters, 2004
    Co-Authors: Sergiy A. Vorobyov, A B Gershman, Zhiquan Luo
    Abstract:

    Adaptive Beamforming methods degrade in the presence of both signal steering vector errors and interference nonstationarity. We develop a new approach to Adaptive Beamforming that is jointly robust against these two phenomena. Our beamformer is based on the optimization of the worst case performance. A computationally efficient convex optimization-based algorithm is proposed to compute the beamformer weights. Computer simulations demonstrate that our beamformer has an improved robustness as compared to other popular robust Beamforming algorithms.

  • Adaptive Beamforming with sidelobe control a second order cone programming approach
    IEEE Signal Processing Letters, 2003
    Co-Authors: Jing Liu, A B Gershman, Zhiquan Luo, K M Wong
    Abstract:

    A new approach to Adaptive Beamforming with sidelobe control is developed. The proposed beamformer represents a modification of the popular minimum variance distortionless response (MVDR) beamformer. It minimizes the array output power while maintaining the distortionless response in the direction of the desired signal and a sidelobe level that is strictly guaranteed to be lower than some given (prescribed) threshold value. The resulting modified MVDR problem is shown to be convex, and its second-order cone (SOC) formulation is obtained that facilitates a computationally efficient way to implement our beamformer using the interior point method.

  • robust Adaptive Beamforming for general rank signal models
    IEEE Transactions on Signal Processing, 2003
    Co-Authors: Shahram Shahbazpanahi, A B Gershman, Zhiquan Luo, K M Wong
    Abstract:

    The performance of Adaptive Beamforming methods is known to degrade severely in the presence of even small mismatches between the actual and presumed array responses to the desired signal. Such mismatches may frequently occur in practical situations because of violation of underlying assumptions on the environment, sources, or sensor array. This is especially true when the desired signal components are present in the beamformer "training" data snapshots because in this case, the Adaptive array performance is very sensitive to array and model imperfections. The similar phenomenon of performance degradation can occur even when the array response to the desired signal is known exactly, but the training sample size is small. We propose a new powerful approach to robust Adaptive Beamforming in the presence of unknown arbitrary-type mismatches of the desired signal array response. Our approach is developed for the most general case of an arbitrary dimension of the desired signal subspace and is applicable to both the rank-one (point source) and higher rank (scattered source/fluctuating wavefront) desired signal models. The proposed robust Adaptive beamformers are based on explicit modeling of uncertainties in the desired signal array response and data covariance matrix as well as worst-case performance optimization. Simple closed-form solutions to the considered robust Adaptive Beamforming problems are derived. Our new beamformers have a computational complexity comparable with that of the traditional Adaptive Beamforming algorithms, while, at the same time, offer a significantly improved robustness and faster convergence rates.

  • Adaptive Beamforming with joint robustness against signal steering vector errors and interference nonstationarity
    International Conference on Acoustics Speech and Signal Processing, 2003
    Co-Authors: Sergiy A. Vorobyov, A B Gershman, Zhiquan Luo
    Abstract:

    Adaptive Beamforming methods are known to degrade in the presence of both signal steering vector errors and interference nonstationarity. In this paper, we develop a new approach to Adaptive Beamforming which is jointly robust against these two phenomena. Our approach is based on the optimization of the worst-case Beamforming performance. A computationally efficient convex optimization based algorithm is proposed to compute the beamformer weights. Computer simulations compare the performance of our algorithm with other robust Adaptive Beamforming techniques.

Sergiy A. Vorobyov - One of the best experts on this subject based on the ideXlab platform.

  • worst case sinr maximization based robust Adaptive Beamforming problem with a nonconvex uncertainty set
    IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, 2019
    Co-Authors: Yongwei Huang, Sergiy A. Vorobyov, Zhiquan Luo
    Abstract:

    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.

  • joint robust transmit receive Adaptive Beamforming for mimo radar using probability constrained optimization
    IEEE Signal Processing Letters, 2016
    Co-Authors: Weiyu Zhang, Sergiy A. Vorobyov
    Abstract:

    In this letter, a joint robust transmit/receive Adaptive Beamforming for multiple-input multiple-output (MIMO) radar based on probability-constrained optimization approach is developed in the case of Gaussian and arbitrary distributed mismatches present in both the transmit and receive signal steering vectors. A tight lower bound of the probability constraint is also derived by using duality theory. The formulated probability-constrained robust Beamforming problem is nonconvex and NP-hard. However, we reformulate its cost function into a bi-quadratic function while the probability constraint splits into transmit and receive parts. Then, a block coordinate descent method based on second-order cone programming is developed to address the biconvex problem. Simulation results show an improved robustness of the proposed Beamforming method as compared to the worst-case and other existing state-of-the-art joint transmit/receive robust Adaptive Beamforming methods for MIMO radar.

  • joint robust transmit receive Adaptive Beamforming for mimo radar using probability constrained optimization
    arXiv: Information Theory, 2015
    Co-Authors: Weiyu Zhang, Sergiy A. Vorobyov
    Abstract:

    A joint robust transmit/receive Adaptive Beamforming for multiple-input multipleoutput (MIMO) radar based on probability-constrained optimization approach is developed in the case of Gaussian and arbitrary distributed mismatch present in both the transmit and receive signal steering vectors. A tight lower bound of the probability constraint is also derived by using duality theory. The formulated probability-constrained robust Beamforming problem is nonconvex and NP-hard. However, we reformulate its cost function into a bi-quadratic function while the probability constraint splits into transmit and receive parts. Then, a block coordinate descent method based on second-order cone programming is developed to address the biconvex problem. Simulation results show an improved robustness of the proposed Beamforming method as compared to the worst-case and other existing state-of-the-art joint transmit/receive robust Adaptive Beamforming methods for MIMO radar.

  • principles of minimum variance robust Adaptive Beamforming design
    Signal Processing, 2013
    Co-Authors: Sergiy A. Vorobyov
    Abstract:

    Robustness is typically understood as an ability of Adaptive Beamforming algorithm to achieve high performance in the situations with imperfect, incomplete, or erroneous knowledge about the source, propagation media, and antenna array. It is also desired to achieve high performance with as little as possible prior information. In the last decade, several fruitful principles to minimum variance distortionless response (MVDR) robust Adaptive Beamforming (RAB) design have been developed and successfully applied to solve a number of problems in a wide range of applications. Such principles of MVDR RAB design are summarized here in a single paper. Prof. Gershman has actively participated in the development and applications of a number of such MVDR RAB design principles.

  • robust Adaptive Beamforming using sequential quadratic programming an iterative solution to the mismatch problem
    IEEE Signal Processing Letters, 2008
    Co-Authors: Aboulnasr Hassanien, Sergiy A. Vorobyov, K M Wong
    Abstract:

    A new approach to the design of robust Adaptive Beamforming is introduced. The essence of the new approach is to estimate the difference between the actual and presumed steering vectors and to use this difference to correct the erroneous presumed steering vector. The estimation process is performed iteratively where a quadratic convex optimization problem is solved at each iteration. Contrary to the worst-case performance-based and the probability-constrained-based approaches, our approach does not make any assumptions on either the norm of the mismatch vector or its probability distribution. Hence, it avoids the need for estimating their values.

A B Gershman - One of the best experts on this subject based on the ideXlab platform.

  • robust Adaptive Beamforming and steering vector estimation in partly calibrated sensor arrays a structured uncertainty approach
    International Conference on Acoustics Speech and Signal Processing, 2010
    Co-Authors: Lei Lei, Joni Polili Lie, A B Gershman, Chong Meng Samson See
    Abstract:

    Two new approaches to Adaptive Beamforming in sparse subarray-based sensor arrays are proposed. Each subarray is assumed to be well calibrated but the intersubarray gain and/or phase mismatches are assumed to remain unknown or imperfectly known. Our first approach is based on a worst-case beamformer design that, unlike the existing worst-case designs, exploits a structured ellipsoidal uncertainty model for the signal steering vector. Our second approach exploits the idea of estimating the signal steering vector by maximizing the output power of the minimum variance beamformer. Several modifications of our second approach are developed for the cases of gain-and-phase and phase-only intersubarray distortions.

  • robust Adaptive Beamforming for hf surface wave over the horizon radar
    IEEE Transactions on Aerospace and Electronic Systems, 2004
    Co-Authors: G A Fabrizio, A B Gershman, M D Turley
    Abstract:

    Adaptive Beamforming is used to enhance the detection of target echoes received by high frequency (HF) surface wave (HFSW) over-the-horizon (OTH) radars in the presence of spatially structured interference. External interference from natural and man-made sources typically masks the entire range-Doppler search space and is characterized by a spatial covariance matrix that is time-varying or nonstationary over the coherent processing interval (CPI). Adaptive beamformers that update the spatial filtering weight vector within the CPI are likely to suppress such interference most effectively, but the intra-CPI antenna pattern fluctuations result in temporal decorrelation of the clutter which severely degrades subclutter visibility after Doppler processing. A robust Adaptive beamformer that effectively suppresses spatially nonstationary interference without degrading subclutter visibility is proposed here. The proposed algorithm is computationally efficient and suitable for practical implementation. Its operational performance is evaluated using experimental data recorded by the Iluka HFSW OTH radar, located near Darwin in far north Australia.

  • Adaptive Beamforming with joint robustness against mismatched signal steering vector and interference nonstationarity
    IEEE Signal Processing Letters, 2004
    Co-Authors: Sergiy A. Vorobyov, A B Gershman, Zhiquan Luo
    Abstract:

    Adaptive Beamforming methods degrade in the presence of both signal steering vector errors and interference nonstationarity. We develop a new approach to Adaptive Beamforming that is jointly robust against these two phenomena. Our beamformer is based on the optimization of the worst case performance. A computationally efficient convex optimization-based algorithm is proposed to compute the beamformer weights. Computer simulations demonstrate that our beamformer has an improved robustness as compared to other popular robust Beamforming algorithms.

  • Adaptive Beamforming with sidelobe control a second order cone programming approach
    IEEE Signal Processing Letters, 2003
    Co-Authors: Jing Liu, A B Gershman, Zhiquan Luo, K M Wong
    Abstract:

    A new approach to Adaptive Beamforming with sidelobe control is developed. The proposed beamformer represents a modification of the popular minimum variance distortionless response (MVDR) beamformer. It minimizes the array output power while maintaining the distortionless response in the direction of the desired signal and a sidelobe level that is strictly guaranteed to be lower than some given (prescribed) threshold value. The resulting modified MVDR problem is shown to be convex, and its second-order cone (SOC) formulation is obtained that facilitates a computationally efficient way to implement our beamformer using the interior point method.

  • robust Adaptive Beamforming for general rank signal models
    IEEE Transactions on Signal Processing, 2003
    Co-Authors: Shahram Shahbazpanahi, A B Gershman, Zhiquan Luo, K M Wong
    Abstract:

    The performance of Adaptive Beamforming methods is known to degrade severely in the presence of even small mismatches between the actual and presumed array responses to the desired signal. Such mismatches may frequently occur in practical situations because of violation of underlying assumptions on the environment, sources, or sensor array. This is especially true when the desired signal components are present in the beamformer "training" data snapshots because in this case, the Adaptive array performance is very sensitive to array and model imperfections. The similar phenomenon of performance degradation can occur even when the array response to the desired signal is known exactly, but the training sample size is small. We propose a new powerful approach to robust Adaptive Beamforming in the presence of unknown arbitrary-type mismatches of the desired signal array response. Our approach is developed for the most general case of an arbitrary dimension of the desired signal subspace and is applicable to both the rank-one (point source) and higher rank (scattered source/fluctuating wavefront) desired signal models. The proposed robust Adaptive beamformers are based on explicit modeling of uncertainties in the desired signal array response and data covariance matrix as well as worst-case performance optimization. Simple closed-form solutions to the considered robust Adaptive Beamforming problems are derived. Our new beamformers have a computational complexity comparable with that of the traditional Adaptive Beamforming algorithms, while, at the same time, offer a significantly improved robustness and faster convergence rates.

Rodrigo C De Lamare - One of the best experts on this subject based on the ideXlab platform.

  • a low complexity variable forgetting factor constant modulus rls algorithm for blind Adaptive Beamforming
    Signal Processing, 2014
    Co-Authors: Boya Qin, Benoit Champagne, Rodrigo C De Lamare, Yunlong Cai, Minjian Zhao
    Abstract:

    Abstract In this paper, a recursive least squares (RLS) based blind Adaptive Beamforming algorithm that features a new variable forgetting factor (VFF) mechanism is presented. The beamformer is designed according to the constrained constant modulus (CCM) criterion, and the proposed Adaptive algorithm operates in the generalized sidelobe canceler (GSC) structure. A detailed study of its operating properties is carried out, including a convexity analysis and a mean squared error (MSE) analysis of its steady-state behavior. The results of numerical experiments demonstrate that the proposed VFF mechanism achieves a superior learning and tracking performance compared to other VFF mechanisms.

  • robust Adaptive Beamforming algorithms using the constrained constant modulus criterion
    Iet Signal Processing, 2014
    Co-Authors: Lukas T N Landau, Rodrigo C De Lamare, Martin Haardt
    Abstract:

    The authors present a robust Adaptive Beamforming algorithm based on the worst-case (WC) criterion and the constrained constant modulus (CCM) approach, which exploits the constant modulus property of the desired signal. Similar to the existing worst-case beamformer with the minimum variance design, the problem can be reformulated as a second-order cone programme and solved with interior point methods. An analysis of the optimisation problem is carried out and conditions are obtained for enforcing its convexity and for adjusting its parameters. Furthermore, low-complexity robust Adaptive Beamforming algorithms based on the modified conjugate gradient and an alternating optimisation strategy are proposed. The proposed low-complexity algorithms can compute the existing WC constrained minimum variance and the proposed WC-CCM designs with a quadratic cost in the number of parameters. Simulations show that the proposed WC-CCM algorithm performs better than existing robust Beamforming algorithms. Moreover, the numerical results also show that the performances of the proposed low-complexity algorithms are equivalent or better than that of existing robust algorithms, whereas the complexity is more than an order of magnitude lower.

  • robust Adaptive Beamforming using a low complexity shrinkage based mismatch estimation algorithm
    IEEE Signal Processing Letters, 2014
    Co-Authors: Hang Ruan, Rodrigo C De Lamare
    Abstract:

    In this work, we propose a low-complexity robust Adaptive Beamforming (RAB) technique which estimates the steering vector using a Low-Complexity Shrinkage-Based Mismatch Estimation (LOCSME) algorithm. The proposed LOCSME algorithm estimates the covariance matrix of the input data and the interference-plus-noise covariance (INC) matrix by using the Oracle Approximating Shrinkage (OAS) method. LOCSME only requires prior knowledge of the angular sector in which the actual steering vector is located and the antenna array geometry. LOCSME does not require a costly optimization algorithm and does not need to know extra information from the interferers, which avoids direction finding for all interferers. Simulations show that LOCSME outperforms previously reported RAB algorithms and has a performance very close to the optimum.

  • low complexity Adaptive step size constrained constant modulus sg algorithms for Adaptive Beamforming
    Signal Processing, 2009
    Co-Authors: Lei Wang, Rodrigo C De Lamare, Yunlong Cai
    Abstract:

    In this paper, we propose two low-complexity Adaptive step size mechanisms to enhance the performance of stochastic gradient (SG) algorithms for Adaptive Beamforming. The beamformer is designed according to the constrained constant modulus (CCM) criterion and the proposed mechanisms are employed in the SG algorithm for implementation. A complexity comparison is provided to show their advantages over existing methods, and a sufficient condition for the convergence of the mean weight vector is established. Theoretical expressions of the excess mean-squared error (EMSE), in both the steady-state and tracking cases, are derived based on the energy conservation approach. The effects of multiple access interference (MAI) and additive noise are considered. Simulation experiments are presented for both the stationary and non-stationary scenarios, illustrating that the proposed algorithms achieve superior performance compared with existing methods, and verifying the accuracy of the analyses.

  • constrained constant modulus rls based blind Adaptive Beamforming algorithm for smart antennas
    International Symposium on Wireless Communication Systems, 2007
    Co-Authors: Lei Wang, Rodrigo C De Lamare
    Abstract:

    In this paper, we study the performance of blind Adaptive Beamforming algorithms for smart antennas in realistic environments. A constrained constant modulus (CCM) design criterion is described and used for deriving a recursive least squares (RLS) type optimization algorithm. Furthermore, two kinds of scenarios are considered in the paper for analyzing its performance. Simulations are performed to compare the performance of the proposed method to other well-known methods for blind Adaptive Beamforming. Results indicate that the proposed method has a significant faster convergence rate, better robustness to changeable environments and better tracking capability.

K M Wong - One of the best experts on this subject based on the ideXlab platform.

  • robust Adaptive Beamforming using sequential quadratic programming an iterative solution to the mismatch problem
    IEEE Signal Processing Letters, 2008
    Co-Authors: Aboulnasr Hassanien, Sergiy A. Vorobyov, K M Wong
    Abstract:

    A new approach to the design of robust Adaptive Beamforming is introduced. The essence of the new approach is to estimate the difference between the actual and presumed steering vectors and to use this difference to correct the erroneous presumed steering vector. The estimation process is performed iteratively where a quadratic convex optimization problem is solved at each iteration. Contrary to the worst-case performance-based and the probability-constrained-based approaches, our approach does not make any assumptions on either the norm of the mismatch vector or its probability distribution. Hence, it avoids the need for estimating their values.

  • Adaptive Beamforming with sidelobe control a second order cone programming approach
    IEEE Signal Processing Letters, 2003
    Co-Authors: Jing Liu, A B Gershman, Zhiquan Luo, K M Wong
    Abstract:

    A new approach to Adaptive Beamforming with sidelobe control is developed. The proposed beamformer represents a modification of the popular minimum variance distortionless response (MVDR) beamformer. It minimizes the array output power while maintaining the distortionless response in the direction of the desired signal and a sidelobe level that is strictly guaranteed to be lower than some given (prescribed) threshold value. The resulting modified MVDR problem is shown to be convex, and its second-order cone (SOC) formulation is obtained that facilitates a computationally efficient way to implement our beamformer using the interior point method.

  • robust Adaptive Beamforming for general rank signal models
    IEEE Transactions on Signal Processing, 2003
    Co-Authors: Shahram Shahbazpanahi, A B Gershman, Zhiquan Luo, K M Wong
    Abstract:

    The performance of Adaptive Beamforming methods is known to degrade severely in the presence of even small mismatches between the actual and presumed array responses to the desired signal. Such mismatches may frequently occur in practical situations because of violation of underlying assumptions on the environment, sources, or sensor array. This is especially true when the desired signal components are present in the beamformer "training" data snapshots because in this case, the Adaptive array performance is very sensitive to array and model imperfections. The similar phenomenon of performance degradation can occur even when the array response to the desired signal is known exactly, but the training sample size is small. We propose a new powerful approach to robust Adaptive Beamforming in the presence of unknown arbitrary-type mismatches of the desired signal array response. Our approach is developed for the most general case of an arbitrary dimension of the desired signal subspace and is applicable to both the rank-one (point source) and higher rank (scattered source/fluctuating wavefront) desired signal models. The proposed robust Adaptive beamformers are based on explicit modeling of uncertainties in the desired signal array response and data covariance matrix as well as worst-case performance optimization. Simple closed-form solutions to the considered robust Adaptive Beamforming problems are derived. Our new beamformers have a computational complexity comparable with that of the traditional Adaptive Beamforming algorithms, while, at the same time, offer a significantly improved robustness and faster convergence rates.

  • blind Adaptive Beamforming for cyclostationary signals
    IEEE Transactions on Signal Processing, 1996
    Co-Authors: K M Wong
    Abstract:

    In order to increase the capacity and to suppress co-channel interference in digital communication systems such as mobile cellular and mobile satellite communication systems, the employment of array Beamforming techniques has been proposed. However, conventional Beamforming methods are not suitable for such cases since these methods were mainly developed for signal detection and direction-of-arrival (DOA) estimation in radar and sonar. In this paper, utilizing the cyclostationary properties of communication signals, we propose three blind cyclic Adaptive Beamforming (CAB) algorithms and their fast implementation schemes. Several numerical examples are included. These results demonstrate that the CAB algorithms are good candidates for spatial reuse of frequency spectrum in digital mobile communication systems of the next generation.