Robust adaptive beamforming for fast moving interference based on the covariance matrix reconstruction

Mohammadzadeh, Saeed; Kükrer, Osman

doi:10.1049/iet-spr.2018.5264

Cited by 14 publications

(6 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This technique is based on using the correlation between the inner products of the steering vector corresponding to a general direction of incidence with the received vectors. Regarding this method, the angle that maximizes the magnitude of the inner product is taken as the DoA estimate in (11) by scanning an angular sector centred on θk as [44]…”

Section: A Estimation Of Ipn Uncertainty Regionmentioning

confidence: 99%

“…It is assumed that, during the observation interval, the interference direction, as observed by the array, stays in the interval ( θk −∆θ k /2, θk +∆θ k /2). In order to estimate ∆θ k , we use the DoA estimation technique in [44]. This technique is based on using the correlation between the inner products of the steering vector corresponding to a general direction of incidence with the received vectors.…”

Section: A Estimation Of Ipn Uncertainty Regionmentioning

confidence: 99%

See 1 more Smart Citation

Covariance Matrix Reconstruction Based on Power Spectral Estimation and Uncertainty Region for Robust Adaptive Beamforming

Mohammadzadeh

Nascimento

Lamare

et al. 2023

IEEE Trans. Aerosp. Electron. Syst.

View full text Add to dashboard Cite

In this work, a simple and effective robust adaptive beamforming technique is proposed for uniform linear arrays, which is based on the power spectral estimation and uncertainty region (PSEUR) of the interference plus noise (IPN) components. In particular, two algorithms are presented to find the angular sector of interference in every snapshot based on the adopted spatial uncertainty region of the interference direction. Moreover, a power spectrum is introduced based on the estimation of the power of interference and noise components, which allows the development of a robust approach to IPN covariance matrix reconstruction. The proposed method has two main advantages. First, an angular region that contains the interference direction is updated based on the statistics of the array data. Secondly, the proposed IPN-PSEUR method avoids estimating the power spectrum of the whole range of possible directions of the interference sector. Simulation results show that the performance of the proposed IPN-PSEUR beamformer is almost always close to the optimal value across a wide range of signal-to-noise ratios.

show abstract

Section: A Estimation Of Ipn Uncertainty Regionmentioning

confidence: 99%

Section: A Estimation Of Ipn Uncertainty Regionmentioning

confidence: 99%

Covariance Matrix Reconstruction Based on Power Spectral Estimation and Uncertainty Region for Robust Adaptive Beamforming

Mohammadzadeh

Nascimento

Lamare

et al. 2023

IEEE Trans. Aerosp. Electron. Syst.

View full text Add to dashboard Cite

show abstract

“…a SVR-CMT algorithm was proposed, which uses CMT method to expand the null and control the sidelobe using inequality constraints, and changes the optimization problem to a standard SVR problem. Reference [14] derived a new method for reconstruction of interference-plus-noise covariance matrix from the simplified power spectral density function.…”

Section: Introductionmentioning

confidence: 99%

Null Broadening Beamforming for Passive Sonar Based on Weighted Similarity Vector

Wang

Zhang

2023

Preprint

View full text Add to dashboard Cite

Beamforming technology is very important for passive sonar to detect targets. However, the performance of beamformer is seriously degraded in practical applications due to the complex and changeable underwater environment. In this paper, a null broadening algorithm for passive sonar based on weighted similarity vector is proposed for underwater fast moving strong interference signals. First, The covariance matrix is reconstructed through the correlation between the steering vector and the subspace eigenvector, which is used to calculate the similarity vector. Then, the maximum power in the interference angle sector is used as the virtual interference source power to broaden the null trap in the angle sector. Next, the difference between the optimal weight vector and the similar vector is minimized, interference-plus-noise power constraints and norm constraints are added, and it is written as a quadratic constrained quadratic programming (QCQP) problem, which is converted into a convex optimization problem by using the semidefinite relaxation technique. Finally, the optimal solution is calculated by using eigen decomposition. The simulation results show that the algorithm can guarantee deep nulling and effectively suppress sidelobe height under various error conditions, which shows that the proposed algorithm has good suppression effect and strong robustness for fast strong interference.

show abstract

“…In reference [13], an SVR-CMT algorithm was proposed that uses the CMT method to expand the null and control the sidelobe using inequality constraints and changes the optimization problem to a standard SVR problem. Reference [14] derived a new method for the reconstruction of the interference-plus-noise covariance matrix from the simplified power spectral density function. This method ensures effectiveness and has less computational complexity.…”

Section: Introductionmentioning

confidence: 99%

Null Broadening Beamforming for Passive Sonar Based on Weighted Similarity Vector

Wang,

Zhang

2023

JMSE

View full text Add to dashboard Cite

Beamforming technology is very important for passive sonar to detect targets. However, the performance of a beamformer is seriously degraded in practical applications due to the complex and changeable underwater environment. In this paper, a null broadening algorithm for passive sonar based on a weighted similarity vector is proposed for underwater fast-moving strong interference signals. First, the covariance matrix was reconstructed through the correlation between the steering vector and the subspace eigenvector, which was used to calculate the similarity vector. Then, the maximum power in the interference angle sector was used as the virtual interference source power to broaden the null in the angle sector. Next, the difference between the optimal weight vector and the similar vector was minimized, the interference-plus-noise power constraints and norm constraints were added, and the equation was written as a quadratic constrained quadratic programming (QCQP) problem, which was converted into a convex optimization problem by using the semidefinite relaxation technique. Finally, the optimal solution was calculated by using eigen decomposition. The simulation results show that the algorithm can guarantee deep nulling and effectively suppress sidelobe height under various error conditions, which shows that the proposed algorithm has a good suppression effect and strong robustness for fast strong interference.

show abstract

Robust adaptive beamforming for fast moving interference based on the covariance matrix reconstruction

Cited by 14 publications

References 29 publications

Covariance Matrix Reconstruction Based on Power Spectral Estimation and Uncertainty Region for Robust Adaptive Beamforming

Covariance Matrix Reconstruction Based on Power Spectral Estimation and Uncertainty Region for Robust Adaptive Beamforming

Null Broadening Beamforming for Passive Sonar Based on Weighted Similarity Vector

Null Broadening Beamforming for Passive Sonar Based on Weighted Similarity Vector

Contact Info

Product

Resources

About