1,187 research outputs found
Steering vector errors and diagonal loading
Diagonal loading is one of the most widely used and effective methods to improve robustness of adaptive beamformers. In this paper, we 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, we address the problem of optimally selecting the loading level with a view to maximise the signal to interference plus noise
ratio in the presence of random steering vector errors. First, we derive an expression for the optimal loading for a given steering vector error and we show that this loading is negative. Next, this 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 optimal loading factor derived herein provides a performance close to optimum
Unit circle MVDR beamformer
The array polynomial is the z-transform of the array weights for a narrowband
planewave beamformer using a uniform linear array (ULA). Evaluating the array
polynomial on the unit circle in the complex plane yields the beampattern. The
locations of the polynomial zeros on the unit circle indicate the nulls of the
beampattern. For planewave signals measured with a ULA, the locations of the
ensemble MVDR polynomial zeros are constrained on the unit circle. However,
sample matrix inversion (SMI) MVDR polynomial zeros generally do not fall on
the unit circle. The proposed unit circle MVDR (UC MVDR) projects the zeros of
the SMI MVDR polynomial radially on the unit circle. This satisfies the
constraint on the zeros of ensemble MVDR polynomial. Numerical simulations show
that the UC MVDR beamformer suppresses interferers better than the SMI MVDR and
the diagonal loaded MVDR beamformer and also improves the white noise gain
(WNG).Comment: Accepted to ICASSP 201
Adaptive beamforming for large arrays in satellite communications systems with dispersed coverage
Conventional multibeam satellite communications systems ensure coverage of wide areas through multiple fixed beams where all users inside a beam share the same bandwidth. We consider a new and more flexible system where each user is assigned his own beam, and the users can be very geographically dispersed. This is achieved through the use of a large direct radiating array (DRA) coupled with adaptive beamforming so as to reject interferences and to provide a maximal gain to the user of interest. New fast-converging adaptive beamforming algorithms are presented, which allow to obtain good signal to interference and noise ratio (SINR) with a number of snapshots much lower than the number of antennas in the array. These beamformers are evaluated on reference scenarios
Performance analysis of beamformers using generalized loading of the covariance matrix in the presence of random steering vector errors
Robust adaptive beamforming is a key issue in array applications where there exist uncertainties about the steering vector of interest. Diagonal loading is one of the most popular techniques to improve robustness. In this paper, we present a theoretical analysis of the signal-to-interference-plus-noise ratio (SINR) for the class of beamformers based on generalized (i.e., not necessarily diagonal) loading of the covariance matrix in the presence of random steering vector errors. A closed-form expression
for the SINR is derived that is shown to accurately predict the SINR obtained in simulations. This theoretical formula is valid for any loading matrix. It provides insights into the influence of the loading matrix and can serve as a helpful guide to select it. Finally, the analysis enables us to predict the level of uncertainties up to which robust beamformers are effective and then depart from the optimal SINR
Performance analysis for a class of robust adaptive beamformers
Robust adaptive beamforming is a key issue in array applications where there exist uncertainties about the steering vector of interest. Diagonal loading is one of the most popular techniques to improve robustness. Recently, worst-case approaches which consist of protecting the array's response in an ellipsoid centered around the nominal steering vector have been proposed. They amount to generalized (i.e. non necessarily diagonal) loading of the covariance matrix. In this paper, we present a theoretical analysis of the signal to interference plus noise ratio (SINR) for this class of robust beamformers, in the presence of random steering vector errors. A closed-form expression for the SINR is derived which is shown to accurately predict the SINR obtained in simulations. This theoretical formula is valid for any loading matrix. It provides insights into the influence of the loading matrix and can serve as a helpful guide to select it. Finally, the analysis enables us to predict the level of uncertainties up to which robust beamformers are effective and then depart from the optimal SINR
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