Adaptive beamforming with the generalized sidelobe canceller in the presence of array imperfections

Jablon, N.K.

doi:10.1109/tap.1986.1143936

Cited by 198 publications

(86 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this case, the latter is considered as an interference, leading to the so-called self-nulling phenomenon [1,2]. Now, mismatches between the actual steering vector and the presumed steering vector are an unavoidable component in most applications using an array of sensors.…”

Section: Introductionmentioning

confidence: 99%

Steering vector errors and diagonal loading

Vincent¹,

Besson²

2004

IEE Proc., Radar Sonar Navig.

View full text Add to dashboard Cite

Diagonal loading is one of the most widely used and effective methods to improve robustness of adaptive beamformers. The authors consider its application to the case of steering vector errors, i.e. when there exists a mismatch between the actual steering vector of interest and the presumed one. More precisely, the problem addressed is that of optimally selecting the loading level with a view to maximising the signal-to-interference-plus-noise ratio in the presence of steering vector errors. First, an expression is derived for the optimal loading for a given steering vector error and it is shown that this loading is negative. Next, random steering errors are considered and the optimal loading is averaged with respect to the probability density function of the steering vector errors, yielding a very simple expression for the average optimal loading. Numerical simulations attest to the validity of the analysis and show that diagonal loading with the derived optimal loading factor provides a performance close to optimum.

show abstract

Section: Introductionmentioning

confidence: 99%

Steering vector errors and diagonal loading

Vincent¹,

Besson²

2004

IEE Proc., Radar Sonar Navig.

View full text Add to dashboard Cite

show abstract

“…During the past decade, several approaches, such as imposing multiple gain constraints in different directions in the vicinity of the presumed SV [1], diagonal loading [2], placing derivative constraints on the presumed SV [3] and eigenspace-based approaches [4], have been proposed to improve the robustness of adaptive beamformer design. The diagonal loading [2] has the advantage of being invariant to the type of mismatches but the choice of the loading factor is not obvious.…”

Section: Introductionmentioning

confidence: 99%

“…The diagonal loading [2] has the advantage of being invariant to the type of mismatches but the choice of the loading factor is not obvious. The authors in [5][6][7] proposed robust capon beamformer (RCB) based on the idea that allows the presumed SV to be within a sphere whose radius determines the uncertainty level.…”

Section: Introductionmentioning

confidence: 99%

“…Since the direction of the signal-of-interest (SOI) is nearer to the presumed look-direction, it is possible to distinguish the null's direction associated to the SOI from the interferences. Although numerous capon beamforming algorithms have been proposed [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15], all these algorithms suffer from the problem of saturation in the output SINR as the input SNR increases.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Robust Adaptive Beamforming Based on Covariance Matrix Reconstruction for Look Direction Mismatch

Mallipeddi¹,

Lie²,

Razul³

et al. 2011

PIER Letters

View full text Add to dashboard Cite

Abstract-The performance degradation in traditional adaptive beamformers can be attributed to the imprecise knowledge of the array steering vector and inaccurate estimation of the covariance matrix. The inaccurate estimation of the covariance matrix is due to the limited data samples and presence of desired signal components in the training data. The mismatch between the actual and presumed steering vectors can be mainly due to the error in the look direction estimate. In this paper, we propose a novel algorithm to estimate the look direction and to reconstruct the covariance matrix so that near optimal performance without the effect of saturation can be achieved as the input SNR increases. Numerical results also show that all existing beamforming algorithms suffer from saturation effect as the input SNR increases.

show abstract

“…However, the adaptive beamformers are rather sensitive to the steering vector mismatches, which will degrade the performance of the adaptive beamformers severely. The causes of steering vectors mismatches in the practical applications include direction-of-arrival (DOA) errors [3], imperfect array calibration and/or distorted antenna shape [4], array manifold mismodeling due to source wavefront distortions resulting from environmental inhomogeneities [5], near-far problem [6], source spreading and local scattering [7] as well as other effects [8]. Therefore, the robustness against the steering vector uncertainties in adaptive beamformers is highly required [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Robust Adaptive Beamforming for Steering Vector Uncertainties Based on Equivalent Doas Method

Gu¹,

Shi²,

Chen³

et al. 2008

PIER

View full text Add to dashboard Cite

Abstract-The adaptive beamformers often suffer severe performance degradation when there exist uncertainties in the steering vector of interest. In this paper, we develop a new approach to robust adaptive beamforming in the presence of an unknown signal steering vector. Based on the observed data, we try to estimate an equivalent directionof-arrival (DOA) for each sensor, in which all factors causing the steering vector uncertainties are ascribed to the DOA uncertainty only. The equivalent DOA of each sensor can be estimated one by one with the assumption that the elements of the steering vector are uncorrelated with each other. Using a Bayesian approach, the equivalent DOA estimator of each sensor is a weighted sum of a set of candidate DOA's, which are combined according to the value of the a posteriori probability for each pointing direction. In this way, the signal steering vector and the diagonal loading sample matrix inversion (DL-SMI) version adaptive beamformer can be obtained. Numerical simulations illustrate the robustness of the proposed beamforming algorithm. 278Gu et al.

show abstract

Adaptive beamforming with the generalized sidelobe canceller in the presence of array imperfections

Cited by 198 publications

References 29 publications

Steering vector errors and diagonal loading

Steering vector errors and diagonal loading

Robust Adaptive Beamforming Based on Covariance Matrix Reconstruction for Look Direction Mismatch

Robust Adaptive Beamforming for Steering Vector Uncertainties Based on Equivalent Doas Method

Contact Info

Product

Resources

About