Coprime beamforming: fast estimation of more sources than sensors

Baxter, William; Nosrati, Hamed; Aboutanios, Elias

doi:10.1049/iet-rsn.2018.5647

Cited by 11 publications

(4 citation statements)

References 31 publications

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“…The complexity of the reconstructed virtual covariance matrix is Oð2M þ N − 1Þ 2 . The step is to perform eigenvalue decomposition and spectral peak search on the matrix, and the complexity is OðF þ 1Þ 3 and…”

Section: Calculate the Weight Vectormentioning

confidence: 99%

“…The essence of beamforming is to adaptively change the weighting factor of each array element according to the signal environment to weigh the array element to achieve the purpose of enhancing the desired signal and suppressing interference. It has been widely used and developed rapidly in the fields of communications, radar, sonar, seismic exploration, radio astronomy, and biomedical engineering [1][2][3][4]. The passive system has simple structure, low complexity, and can implement concealed reception, which is widely favoured.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive beamforming algorithm for coprime array based on interference and noise covariance matrix reconstruction

Cui

et al. 2021

IET Radar Sonar & Navi

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Compared with the uniform linear array (ULA) under the same physical array element, the coprime array can obtain a higher degree of freedom and a larger array aperture, so it has received extensive attention from scholars in recent years. Based on the idea of interference plus noise covariance matrix (INCM) reconstruction, this study proposed a robust adaptive beamforming algorithm using the coprime array. In the virtual array generated by the coprime array, the authors prove from the perspective of virtual signals that the constructed Toeplitz matrix is equivalent to a virtual ULA with more array elements. Subsequently, a high-resolution subspace algorithm is used to estimate the direction of arrival (DOA) of the signals, and the estimated DOA is used to reconstruct the direction vector (DV) of the coprime array. Through the eigenvalue decomposition of the reconstructed Toeplitz matrix, the corresponding relationship between the eigenvalue and the power is deduced, and the signals and noise power are estimated using the eigenvalue. Finally, combine the DV of the desired signal and the reconstructed INCM to calculate the weight vector of the proposed algorithm. The simulation results prove the superiority and effectiveness of the algorithm.

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Section: Calculate the Weight Vectormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Adaptive beamforming algorithm for coprime array based on interference and noise covariance matrix reconstruction

Cui

et al. 2021

IET Radar Sonar & Navi

View full text Add to dashboard Cite

show abstract

“…e essence of beamforming is to adaptively change the weighting factors of each array element according to the signal environment, thereby weighting the array elements and performing spatial filtering to achieve the purpose of enhancing the desired signal and suppressing interference. It has been widely used and developed rapidly in the fields of communications, radar, sonar, seismic prospecting, radio astronomy, and biomedical engineering [1][2][3][4]. However, theory and practice prove that beamforming is very sensitive to the problem of signal model mismatch.…”

Section: Introductionmentioning

confidence: 99%

Robust Adaptive Beamforming Algorithm for Sparse Array Based on Covariance Matrix Reconstruction Technology

Cui

Wang

et al. 2022

International Journal of Antennas and Propagation

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When the array structure of the sparse arrays (SA) cannot be determined, the existing beamforming algorithms designed according to specific formations such as coprime arrays (CA), nested arrays (NA), etc. will fail. To solve this problem, we propose two algorithms that are suitable for a variety of SA. In the first method, assuming that the desired signal is a non-Gaussian signal, the desired signal direction vector (DSDV) is estimated using the fourth-order cumulant, and then the interference plus noise covariance matrix (INCM) is reconstructed by integrating the area outside the desired signal. When the desired signal is a Gaussian signal, we propose the second method. The second method estimates the power and direction of arrival (DOA) of the signals by performing eigenvalue decomposition on the sampled covariance matrix (SCM) and finally calculates the weight vector. However, this method needs to estimate the DOA of the signals, so it has certain requirements for the SA structure design. The simulation results show that the proposed method has good performance and strong robustness under different SA.

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“…It has been shown that the clutter or interference suppression performance potentials can be increased using the virtual coarray model at a low cost and low hardware complexity [10][11][12][13]. Compared with the nested arrays, coprime arrays can reduce the mutual coupling (MC) effects as the result of the larger inter-sensor spacing [14][15][16]. To date, several STAP algorithms have been developed for clutter suppression in airborne radar with coprime arrays [17,18].…”

Section: Introductionmentioning

confidence: 99%

Robust space‐time adaptive processing for airborne radar with coprime arrays in presence of gain and phase errors

Yang

Huang

2020

IET Radar Sonar & Navi

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Space‐time adaptive processing (STAP) for sparse arrays such as coprime and nested arrays is shown to have improved performance for clutter suppression in airborne radar as compared with uniform linear arrays with the same size. However, most of the existing STAP algorithms are derived based on the assumption that the array manifold is exactly known. In this study, a robust STAP algorithm for the clutter suppression with coprime arrays in the presence of gain and phase (GP) errors is proposed. In particular, the virtual space‐time signal with GP errors is first formulated using the covariance matrix of the perturbed array received data under the Gaussian distributed interference assumption. Then, the problem of the clutter spectrum and GP errors' estimates is formulated as a joint minimization optimization problem by exploiting the sparsity of the clutter in the angle–Doppler plane. Finally, a two‐step approach based on the sparse recovery and least‐squared method is developed to solve the resultant optimization problem and the STAP filter can be designed using the clutter spectrum and GP errors' estimates. Compared with the existing sparsity‐based STAP algorithms for coprime arrays, the proposed algorithm shows more robustness to GP errors as illustrated by simulation results.

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Coprime beamforming: fast estimation of more sources than sensors

Cited by 11 publications

References 31 publications

Adaptive beamforming algorithm for coprime array based on interference and noise covariance matrix reconstruction

Adaptive beamforming algorithm for coprime array based on interference and noise covariance matrix reconstruction

Robust Adaptive Beamforming Algorithm for Sparse Array Based on Covariance Matrix Reconstruction Technology

Robust space‐time adaptive processing for airborne radar with coprime arrays in presence of gain and phase errors

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