Maximally Robust Capon Beamformer

Rubsamen, M.; Pesavento, Marius

doi:10.1109/tsp.2013.2242067

Cited by 29 publications

(18 citation statements)

References 26 publications

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“…To mitigate this problem, the NCCB method imposes a norm constraint on the weight vector, and [12] proposes to calculate the Capon beamformer with the minimum sensitivity to model errors by minimising the norm of the adaptive weights, considering the uncertainty set for the signal steering vector. In order to avoid an arbitrarily low sensitivity achieved by scaling the beamformer's weight vector without changing the output SINR performance, it is important to define the beamformer's sensitivity as the squared norm of the scaled weight vectorw = w/(â H w), which satisfiesw Hâ = 1 [12].…”

Section: Hâmentioning

confidence: 99%

“…In order to avoid an arbitrarily low sensitivity achieved by scaling the beamformer's weight vector without changing the output SINR performance, it is important to define the beamformer's sensitivity as the squared norm of the scaled weight vectorw = w/(â H w), which satisfiesw Hâ = 1 [12]. This leads to the definition of the beamformer's sensitivity as [1,9,12] …”

Section: Hâmentioning

confidence: 99%

“…However, this assumption may not be valid due to factors such as direction-of-arrival (DOA) mismatch error, array calibration error, local scattering, near-far spatial signature mismatch and finite sample effect [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17]. Whenever this happens, the output signal-to-interference-plus-noise ratio (SINR) of the SCB degrades significantly.…”

Section: Introductionmentioning

confidence: 99%

“…Since the actual steering vector lies within the signal-plusinterference (SI) subspace, the idea of ESB is to project the presumed steering vector onto the SI subspace to give a better estimation of the real steering vector of the SOI. However, the ESB suffers from severe performance degradation if the dimension of the SI subspace, i.e., the number of sources, can not be estimated correctly [11,12]. Two well-known methods for estimating the number of sources are the Akaike information criterion (AIC) and the minimum description length (MDL).…”

Section: Introductionmentioning

confidence: 99%

“…However, if the SI subspace is overestimated or underestimated, there will be a large error in the projected steering vector. In such a case, the beamformer's sensitivity can become very large [1,9,12]. Based on the minimum sensitivity criterion, we here develop a novel method to estimate the dimension of the SI subspace to improve the robustness of the ESB.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Minimum sensitivity based robust beamforming with eigenspace decomposition

Wang

Zhang²,

Liu

2016

Multidim Syst Sign Process

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An enhanced eigenspace-based beamformer (ESB) derived using the minimum sensitivity criterion is proposed with significantly improved robustness against steering vector errors. The sensitivity function is defined as the squared norm of the appropriately scaled weight vector and since the sensitivity function of an array to perturbations becomes very large in the presence of steering vector errors, it can be used to find the best projection for the ESB, irrespective of the distribution of additive noises. As demonstrated by simulation results, the proposed method has a better performance than the classic ESBs and the previously proposed uncertainty set based approach.

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Section: Hâmentioning

confidence: 99%

Section: Hâmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Minimum sensitivity based robust beamforming with eigenspace decomposition

Wang

Zhang²,

Liu

2016

Multidim Syst Sign Process

View full text Add to dashboard Cite

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Robust beamforming with imprecise array geometry using steering vector estimation and interference covariance matrix reconstruction

Yang

Liao

et al. 2015

Multidim Syst Sign Process

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Compared with state-of-the-art robust adaptive beamforming methods, the recently developed steering vector estimation-based beamformer and interference-plus-noise covariance matrix reconstruction-based beamformer are known to provide better robustness against model mismatch. However, both methods may suffer from performance degradation in presence of antenna array geometry error. To alleviate this problem, a novel algorithm is proposed in this paper. In contrast to previous works, the true desired signal steering vector is estimated by solving a new optimization problem, the objective function of which is minimizing the beamformer sensitivity, while the constraint of which is obtained by optimizing the worst-case performance of the constraint adopted in conventional steering vector estimationbased method. We show that the solution of the newly constructed optimization problem can be obtained in closed form by using the Lagrange multiplier methodology, which has a comparable computational complexity with that of the standard Capon beamformer. Subsequently, the precise interference-plus-noise covariance matrix is reconstructed by eliminating the estimated desired signal component from the sample covariance matrix, which mitigates 123Multidim Syst Sign Process computational burden and accumulated errors induced by the integration process employed in the conventional covariance matrix reconstruction-based method. Owing to the high accuracy of estimated and reconstructed results, the proposed robust adaptive beamforming algorithm can maintain satisfactory performance in cases with imprecise array geometry. Simulation results verify that the proposed method outperforms the existing ones in many scenarios.

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Signal enhancement techniques for through-the-earth communication based on multiple references and beamforming

Jiang

Zhao

et al. 2018

AEU - International Journal of Electronics and Communications

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Maximally Robust Capon Beamformer

Cited by 29 publications

References 26 publications

Minimum sensitivity based robust beamforming with eigenspace decomposition

Minimum sensitivity based robust beamforming with eigenspace decomposition

Robust beamforming with imprecise array geometry using steering vector estimation and interference covariance matrix reconstruction

Signal enhancement techniques for through-the-earth communication based on multiple references and beamforming

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