Sentence examples for random steering 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).

It can be viewed as a mixture of conditional MMSE estimates weighted by the posterior probability density function of the random steering vector given the observed data.

By assuming that the steering vector is a complex Gaussian random vector, the beamformer can be viewed as a mixture of conditional MMSE estimates weighted by the posterior PDF of the random steering vector.

As stated in [27, 28], this Gaussian random model for steering vector is general because it can address the uncertainties due to many reasons, including DOA pointing error, array calibration error, scattering around the source, propagation through an inhomogeneous medium, and other systematic problems.

To this end, at the design stage it is assumed that the CUT contains a random interferer with its steering vector unknown.

Unlike DOA, random modeling for the steering vector can address not only the uncertainties due to pointing error but also the uncertainties due to scattering around the source, miscalibration, array deformation, different gain, and phase responses of sensors in the array, etc.

ANOVA with a fish identifier as a random factor and turn direction (left vs. right) as a fixed factor was used to test for inter-individual differences in steering angle.

The decision to exclude a patient was taken by an independent steering committee before unblinding of the random code.

The independent statistician on the trial steering committee selected one allocation at random for each set and randomly assigned intervention or control to the two groups in each set to construct the final allocation.

A typical analytical model mathematically expresses the Multiple-Input Multiple-Output (MIMO) channel matrix as a function of a random Gaussian-fading matrix and various channel correlations or steering vectors, without specifying any value for these parameters.

The steering vector is modeled as a complex Gaussian random vector that characterizes the level of steering vector uncertainty.