Sentence examples for minimum variance beamformer from inspiring English sources

In this paper, random steering vector mismatches in sensor arrays are considered and probability constraints are imposed for designing a robust minimum variance beamformer (RMVB).

The new method is tested with biophysically realistic simulated data and the results are compared to those obtained with traditional spatial approaches like the popular Low Resolution Electromagnetic TomogrAphy (LORETA) and minimum variance Beamformer.

Simulations show that the array gain of the proposed beamformer exceeds that of the classical minimum variance beamformer for a finite number of samples and coherent interference scenario, using the sample matrix inverse technique or the diagonal loading approach.

We present a minimum variance beamformer with a pre-specified suppression level over a pre-defined angular null sector, which for example may be used when the interference moves across an a-priori known angular sector.

SAM is a spatial filtering technique based on the linear constrained minimum variance beamformer.

MEG data were source modelled using a vectorised linearly constrained minimum variance beamformer, 20 using a multiple-spheres head model.

Initial spatial analyses were performed using a novel application of a minimum-variance beamformer algorithm (synthetic aperture magnetometry: SAM) [ 52- 54].

For comparison purposes, we also display the performances of the linearly constrained minimum variance (LCMV) beamformer [29], subspace projection beamformer [22], and other three Bayesian beamformers proposed in [25, 26, 28], where the beamformers of [22, 28, 29] are non-recursive STI block-based methods and the beamformers of [25, 26] are recursive.

This paper introduces a new beamformer, which combines the eigenspace based minimum variance (ESBMV) beamformer with a subarray coherence based postfilter (SCBP), for improving the quality of ultrasound plane-wave imaging.

Typical supervised signal extraction approaches are, e.g., multichannel Wiener filtering (MWF) [8, 9] or beamforming approaches, such as linearly constrained minimum variance (LCMV) beamformer [10].

Improving the robustness of MVDR beamformers has commonly been achieved by adding appropriate constraints in the determination of beamforming weights, such as the linearly constrained minimum variance (LCMV) beamformer.