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Moeness G Amin - One of the best experts on this subject based on the ideXlab platform.
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hybrid Sparse Array beamforming design for general rank signal models
IEEE Transactions on Signal Processing, 2019Co-Authors: Syed A. Hamza, Moeness G AminAbstract:The paper considers Sparse Array design for receive beamforming achieving maximum signal-to-interference plus noise ratio (MaxSINR) for both single point source and multiple point sources, operating in an interference active environment. Unlike existing Sparse design methods which either deal with structured environment-independent or non-structured environment-dependent Arrays, our method is a hybrid approach and seeks a full augumentable Array that optimizes beamformer performance. This approach proves important for limited aperture that constrains the number of possible uniform grid points for sensor placements. The problem is formulated as quadratically constraint quadratic program (QCQP), with the cost function penalized with weighted $l_1$ -norm squared of the beamformer weight vector. Simulation results are presented to show the effectiveness of the proposed algorithms for Array configurability in the case of both single and general rank signal correlation matrices. Performance comparisons among the proposed Sparse Array, the commonly used uniform Arrays, Arrays obtained by other design methods, and Arrays designed without the augmentability constraint are provided.
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Sparse Array design utilizing matrix completion
Asilomar Conference on Signals Systems and Computers, 2019Co-Authors: Syed A. Hamza, Moeness G AminAbstract:Sparse Array design has been advantageous in re¬ducing receiver data, system’s hardware and computational costs by the careful placement of available sensors such that the ob¬jective function is optimized. In this paper, we investigate Sparse Array design for maximizing the Signal-to-Interference plus noise ratio (SINR) which arises frequently in many applications. We propose a design approach which does not necessarily require any a priori knowledge of the interference environment and operates directly on the received data statistics. The data dependent design is achieved by adopting a low rank matrix completion, which ensures the availability of full data correlation matrix against all possible locations. The regularized successive convex approximation (SCA) is utilized to realize Sparse beamformer design. We compare the performance of Sparse Array design with the commonly used Arrays in terms of maximizing the SINR and show the effectiveness of the proposed algorithm under limited received data snapshots.
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Sparse Array Design for Maximizing the Signal-to-Interference-plus-Noise-Ratio by Matrix Completion
arXiv: Signal Processing, 2019Co-Authors: Syed A. Hamza, Moeness G AminAbstract:We consider Sparse Array beamfomer design achieving maximum signal-to interference plus noise ratio (MaxSINR). Both Array configuration and weights are attuned to the changing sensing environment. This is accomplished by simultaneously switching among antenna positions and adjusting the corresponding weights. The Sparse Array optimization design requires estimating the data autocorrelations at all spatial lags across the Array aperture. Towards this end, we adopt low rank matrix completion under the semidefinite Toeplitz constraint for interpolating those autocorrelation values corresponding to the missing lags. We compare the performance of matrix completion approach with that of the fully augmentable Sparse Array design acting on the same objective function. The optimization tool employed is the regularized $l_1$-norm successive convex approximation (SCA). Design examples with simulated data are presented using different operating scenarios, along with performance comparisons among various configurations.
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doa estimation exploiting Sparse Array motions
IEEE Transactions on Signal Processing, 2019Co-Authors: Guodong Qin, Moeness G Amin, Yimin D ZhangAbstract:This paper utilizes Sparse Array motion to increase the numbers of achievable both degrees of freedom (DOFs) and consecutive lags in direction-of-arrival (DOA) estimation problems. We use commonly employed environment-independent Sparse Array configurations. The design of these Arrays is not dependent on the sources in the field of view, but rather aims at achieving desirable difference co-Arrays. They include structured coprime and nested Arrays, minimum redundancy Array (MRA), minimum hole Array (MHA), and Sparse uniform linear Array (SULA). Array motion can fill the holes in the spatial autocorrelation lags associated with a fixed platform and, therefore, increases the number of sources detectable by the same number of Array sensors. Quasi-stationarity of the environment is assumed where the source locations and waveforms are considered invariant over Array motion of half wavelength. Closed-form expressions of the number of DOFs and consecutive spatial correlation lags for coprime and nested Arrays as well as SULA, due to Array translation motion, are derived. The number of DOFs and consecutive lags for the specific cases of MRA an 5 avaluated. We show the respective DOA estimation performance based on Sparse reconstruction techniques.
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Sparse Array dft beamformers for wideband sources
IEEE Radar Conference, 2019Co-Authors: Syed A. Hamza, Moeness G AminAbstract:Sparse Arrays are popular for performance optimization while keeping the hardware and computational costs down. In this paper, we consider Sparse Arrays design method for wideband source operating in a wideband jamming environment. Maximizing the signal-to-interference plus noise ratio (MaxSINR) is adopted as an optimization objective for wideband beamforming. Sparse Array design problem is formulated in the DFT domain to process the source as parallel narrowband sources. The problem is formulated as quadratically constraint quadratic program (QCQP) alongside the weighted mixed $l_{1-\infty}$ -norm squared penalization of the beamformer weight vector. The semidefinite relaxation (SDR) of QCQP promotes Sparse solutions by iteratively re-weighting beamformer based on previous iteration. It is shown that the DFT approach reduces the computational cost considerably as compared to the delay line approach, while efficiently utilizing the degrees of freedom to harness the maximum output SINR offered by the given Array aperture.
Xiangrong Wang - One of the best experts on this subject based on the ideXlab platform.
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dual function mimo radar communications system design via Sparse Array optimization
arXiv: Signal Processing, 2018Co-Authors: Xiangrong Wang, Aboulnasr Hassanien, Moeness G AminAbstract:Spectrum congestion and competition over frequency bandwidth could be alleviated by deploying dual-function radar-communications systems, where the radar platform presents itself as a system of opportunity to secondary communication functions. In this paper, we propose a new technique for communication information embedding into the emission of multiple-input multiple-output (MIMO) radar using Sparse antenna Array configurations. The phases induced by antenna displacements in a sensor Array are unique, which makes Array configuration feasible for symbol embedding. We also exploit the fact that in a MIMO radar system, the association of independent waveforms with the transmit antennas can change over different pulse repetition periods without impacting the radar functionality. We show that by reconfiguring Sparse transmit Array through antenna selection and reordering waveform-antenna paring, a data rate of megabits per second can be achieved for a moderate number of transmit antennas. To counteract practical implementation issues, we propose a regularized antenna selection based signaling scheme. The possible data rate is analyzed and the symbol/bit error rates are derived. Simulation examples are provided for performance evaluations and to demonstrate the effectiveness of proposed DFRC techniques.
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robust Sparse Array design for adaptive beamforming against doa mismatch
Signal Processing, 2018Co-Authors: Xiangrong Wang, Moeness G Amin, Xianghua WangAbstract:Abstract The performance of an adaptive beamformer is significantly influenced by its Array configuration. The problem of optimum Array configuration for minimum variance distortionless response (MVDR) beamformers has been recently investigated under the assumption of accurate estimate or prior exact knowledge of the source direction of arrival (DOA). Inaccuracies in DOA can lead to significant performance degradation. Improving the robustness of MVDR beamformers has commonly been achieved by adding appropriate constraints in the determination of beamforming weights, such as the linearly constrained minimum variance (LCMV) beamformer. This work examines the sensitivity of different Sparse Array configurations towards uncertainty in the source DOA. It proposes enhancing system robustness through optimizing Array configurations. The Sparse Array design problem is formulated in terms of maximizing the output signal-to-interference-plus-noise ratio (SINR) of the MVDR and LCMV beamformers. The constrained maximization problem is expressed as the fraction of matrix determinants, and a sequential convex programming algorithm is adopted for the solution of the corresponding non-convex problem. Numerical examples are presented to validate the robustness of configured Sparse Array MVDR and LCMV beamformers for small errors in source directional angles.
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optimum Sparse Array design for multiple beamformers with common receiver
International Conference on Acoustics Speech and Signal Processing, 2018Co-Authors: Xiangrong Wang, Moeness G Amin, Xianghua WangAbstract:The problem of optimum Sparse Array beamformer design to maximize output signal-to-interference-plus-noise ratio (SINR) in the case of multiple narrowband sources was recently investigated. This was based on seeking both optimum sensor placement as well as optimum a single beamformer for all sources in the Array field of view. In this paper, we consider multiple beamformers with a common Sparse Array. That is, we deal with a more prevalent case in radar and communications where each source is assigned its own beam. This could be the case for both switched and simultaneous or staring beams. The paper considers optimum Sparse Array design for both narrowband and wideband sources. Analysis and simulation examples demonstrate that the optimum Sparse Array configuration depends on both the arrival angle and the frequency of the incoming signal and it plays a vital role in determining the performance of multiple beamformer receivers.
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Analysis and Design of Optimum Sparse Array Configurations for Adaptive Beamforming
IEEE Transactions on Signal Processing, 2018Co-Authors: Xiangrong Wang, Moeness G Amin, Xianbin CaoAbstract:In this paper, we analyze the effect of nonuniform Array configurations on adaptive beamforming for enhanced signal-to-interference-plus-noise ratio (SINR). The Array is configured using a given number of antennas or through a selection of subset of antennas from a larger available set, leading to a Sparse Array in both cases. The bounds on the highest achievable SINR for a given number of antennas are formulated and used to offer new insights into open-loop adaptive beamforming. The upper and lower bounds underscore the role of Array configurations in optimizing performance for interference-free and interference-active environments, respectively. This paper considers the general case of multiple sources and interferences in the field of view. We formulate three angles, namely eigenspace angle, conventional angle, and minimum canonical angle for characterizing spatial separation between the source and interference subspaces, which represent performance loss incurred by interference nulling. Three Sparse Array design methods, incorporating these angles, are proposed. Simulation examples confirm the role of subspace angles in optimum beamforming and validate the utility of antenna selection algorithms for Sparse Array design.
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Sparse Array quiescent beamformer design combining adaptive and deterministic constraints
IEEE Transactions on Antennas and Propagation, 2017Co-Authors: Xiangrong Wang, Moeness G Amin, Xianghua Wang, Xianbin CaoAbstract:In this paper, we examine Sparse Array quiescent beamforming for multiple sources in interference-free environment. To maximize the output signal-to-noise ratio (SNR), the beamformer design comprises two intertwined stages, the determination of beamforming weights and the reconfiguration of Array structure. The SNR maximization may produce high sidelobe levels, making the receiver vulnerable to interferences. We consider the problem of achieving maximum SNR beamforming subject to specified quiescent pattern constraints and, as such, combine both adaptive and deterministic approaches for Sparse Array configurations. We employ two convex relaxation methods and an iterative linear fractional programming algorithm to solve the nonconvex antenna selection problem for Sparse Array beamformers. Simulation examples demonstrate that the Array configuration plays a vital role in determining the beamforming performance in interference-free scenarios.
Xiqin Wang - One of the best experts on this subject based on the ideXlab platform.
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doa estimation of coherent signals on coprime Arrays exploiting fourth order cumulants
Sensors, 2017Co-Authors: Yimin Liu, Xiqin WangAbstract:This paper considers the problem of direction-of-arrival (DOA) estimation of coherent signals on passive coprime Arrays, where we resort to the fourth-order cumulants of the received signal to explore more information. A fourth-order cumulant matrix (FCM) is introduced for the coprime Array. The special structure of the FCM is combined with the Array configuration to resolve the coherent signals. Since each Sparse Array of a coprime Array is uniform, a series of overlapping identical subArrays can be extracted. Using this property, we propose a generalized spatial smoothing scheme applied to the FCM. From the smoothed FCM, the DOAs of both the coherent and independent signals can be successfully estimated on the pseudo-spectrum generated by the fourth-order MUSIC algorithm. To overcome the problem of occasional false peaks appearing on the pseudo-spectrum, we use a supplementary Sparse Array whose inter-sensor spacing is coprime to that of either existing Sparse Array. From the combined spectrum aided by the supplementary sensors, the false peaks are removed while the true peaks remain. The effectiveness of the proposed methods is demonstrated by simulation examples.
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doa estimation of coherent signals on coprime Arrays exploiting fourth order cumulants
arXiv: Information Theory, 2016Co-Authors: Yimin Liu, Xiqin WangAbstract:This paper considers the problem of direction-of-arrival (DOA) estimation of coherent signals on passive coprime Arrays, where we resort to the fourth-order cumulants of the received signal to explore more information. A fourth-order cumulant matrix (FCM) is introduced for the coprime Arrays. The special structure of the FCM is combined with the Array configuration to resolve the coherent signals. Since each Sparse Array of the coprime Arrays is uniform, a series of overlapping identical subArrays can be extracted. Using this property, we propose a generalized spatial smoothing scheme applied to the FCM. From the smoothed FCM, the DOAs of both the coherent and independent signals can be successfully estimated on the pseudo-spectrum generated by the fourth-order MUSIC algorithm. To overcome the problem of occasional false peak appearing on the pseudo-spectrum, we use a supplementary Sparse Array whose inter-sensor spacing is coprime to that of either existing Sparse Array. From the combined spectrum aided by the supplementary sensors, the false peaks are removed while the true peaks remain. The effectiveness of the proposed methods is demonstrated by simulation examples.
Xianghua Wang - One of the best experts on this subject based on the ideXlab platform.
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robust Sparse Array design for adaptive beamforming against doa mismatch
Signal Processing, 2018Co-Authors: Xiangrong Wang, Moeness G Amin, Xianghua WangAbstract:Abstract The performance of an adaptive beamformer is significantly influenced by its Array configuration. The problem of optimum Array configuration for minimum variance distortionless response (MVDR) beamformers has been recently investigated under the assumption of accurate estimate or prior exact knowledge of the source direction of arrival (DOA). Inaccuracies in DOA can lead to significant performance degradation. Improving the robustness of MVDR beamformers has commonly been achieved by adding appropriate constraints in the determination of beamforming weights, such as the linearly constrained minimum variance (LCMV) beamformer. This work examines the sensitivity of different Sparse Array configurations towards uncertainty in the source DOA. It proposes enhancing system robustness through optimizing Array configurations. The Sparse Array design problem is formulated in terms of maximizing the output signal-to-interference-plus-noise ratio (SINR) of the MVDR and LCMV beamformers. The constrained maximization problem is expressed as the fraction of matrix determinants, and a sequential convex programming algorithm is adopted for the solution of the corresponding non-convex problem. Numerical examples are presented to validate the robustness of configured Sparse Array MVDR and LCMV beamformers for small errors in source directional angles.
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optimum Sparse Array design for multiple beamformers with common receiver
International Conference on Acoustics Speech and Signal Processing, 2018Co-Authors: Xiangrong Wang, Moeness G Amin, Xianghua WangAbstract:The problem of optimum Sparse Array beamformer design to maximize output signal-to-interference-plus-noise ratio (SINR) in the case of multiple narrowband sources was recently investigated. This was based on seeking both optimum sensor placement as well as optimum a single beamformer for all sources in the Array field of view. In this paper, we consider multiple beamformers with a common Sparse Array. That is, we deal with a more prevalent case in radar and communications where each source is assigned its own beam. This could be the case for both switched and simultaneous or staring beams. The paper considers optimum Sparse Array design for both narrowband and wideband sources. Analysis and simulation examples demonstrate that the optimum Sparse Array configuration depends on both the arrival angle and the frequency of the incoming signal and it plays a vital role in determining the performance of multiple beamformer receivers.
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Sparse Array quiescent beamformer design combining adaptive and deterministic constraints
IEEE Transactions on Antennas and Propagation, 2017Co-Authors: Xiangrong Wang, Moeness G Amin, Xianghua Wang, Xianbin CaoAbstract:In this paper, we examine Sparse Array quiescent beamforming for multiple sources in interference-free environment. To maximize the output signal-to-noise ratio (SNR), the beamformer design comprises two intertwined stages, the determination of beamforming weights and the reconfiguration of Array structure. The SNR maximization may produce high sidelobe levels, making the receiver vulnerable to interferences. We consider the problem of achieving maximum SNR beamforming subject to specified quiescent pattern constraints and, as such, combine both adaptive and deterministic approaches for Sparse Array configurations. We employ two convex relaxation methods and an iterative linear fractional programming algorithm to solve the nonconvex antenna selection problem for Sparse Array beamformers. Simulation examples demonstrate that the Array configuration plays a vital role in determining the beamforming performance in interference-free scenarios.
Yimin Liu - One of the best experts on this subject based on the ideXlab platform.
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hardware prototype demonstration of a cognitive radar with Sparse Array antennas
Electronics Letters, 2020Co-Authors: Satish Mulleti, Tianyao Huang, Yimin Liu, Yonina C EldarAbstract:As a typical signal processing problem, direction-of-arrival (DOA) estimation has been adapted to a wide range of applications in radar-based systems. A high DOA resolution requires a large number of antenna elements which increases the overall cost. To minimise the cost, it is desirable to choose an optimum sub-Array from a full Array. To enable cognition, the subArrays are selected based on the present target scenario. By using deep learning (DL) based techniques, the authors show a cognitive Sparse Array selection technique. By using hardware simulations, they demonstrate the applicability of the deep learning (DL)-based Sparse antenna selection network in direction-of-arrival (DOA) estimation problems. They show that the DL-based sub-Arrays lead to a higher direction-of-arrival (DOA) estimation accuracy by 6 dB over random Array selection.
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doa estimation of coherent signals on coprime Arrays exploiting fourth order cumulants
Sensors, 2017Co-Authors: Yimin Liu, Xiqin WangAbstract:This paper considers the problem of direction-of-arrival (DOA) estimation of coherent signals on passive coprime Arrays, where we resort to the fourth-order cumulants of the received signal to explore more information. A fourth-order cumulant matrix (FCM) is introduced for the coprime Array. The special structure of the FCM is combined with the Array configuration to resolve the coherent signals. Since each Sparse Array of a coprime Array is uniform, a series of overlapping identical subArrays can be extracted. Using this property, we propose a generalized spatial smoothing scheme applied to the FCM. From the smoothed FCM, the DOAs of both the coherent and independent signals can be successfully estimated on the pseudo-spectrum generated by the fourth-order MUSIC algorithm. To overcome the problem of occasional false peaks appearing on the pseudo-spectrum, we use a supplementary Sparse Array whose inter-sensor spacing is coprime to that of either existing Sparse Array. From the combined spectrum aided by the supplementary sensors, the false peaks are removed while the true peaks remain. The effectiveness of the proposed methods is demonstrated by simulation examples.
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doa estimation of coherent signals on coprime Arrays exploiting fourth order cumulants
arXiv: Information Theory, 2016Co-Authors: Yimin Liu, Xiqin WangAbstract:This paper considers the problem of direction-of-arrival (DOA) estimation of coherent signals on passive coprime Arrays, where we resort to the fourth-order cumulants of the received signal to explore more information. A fourth-order cumulant matrix (FCM) is introduced for the coprime Arrays. The special structure of the FCM is combined with the Array configuration to resolve the coherent signals. Since each Sparse Array of the coprime Arrays is uniform, a series of overlapping identical subArrays can be extracted. Using this property, we propose a generalized spatial smoothing scheme applied to the FCM. From the smoothed FCM, the DOAs of both the coherent and independent signals can be successfully estimated on the pseudo-spectrum generated by the fourth-order MUSIC algorithm. To overcome the problem of occasional false peak appearing on the pseudo-spectrum, we use a supplementary Sparse Array whose inter-sensor spacing is coprime to that of either existing Sparse Array. From the combined spectrum aided by the supplementary sensors, the false peaks are removed while the true peaks remain. The effectiveness of the proposed methods is demonstrated by simulation examples.