Two novel adaptive multiple-beamformers for reception of coherent signals with known directions-of-arrival (DOAs) in the presence of uncorrelated interferences are proposed. The first approach is a two-step solution as follows: estimate the amplitudes of all the coherent signals using a subspace method and then construct the linearly constrained minimum variance beamformer from the generalized ...

Two novel adaptive multiple-beamformers for reception of coherent signals with known directions-of-arrival (DOAs) in the presence of uncorrelated interferences are proposed. The first approach is a two-step solution as follows: estimate the amplitudes of all the coherent signals using a subspace method and then construct the linearly constrained minimum variance beamformer from the generalized ...

Linearly constrained minimum variance beamformers are highly effective for analysis of weakly correlated brain activity, but their performance degrades when correlations become significant. Multiple constrained minimum variance (MCMV) beamformers are insensitive to source correlations but require a priori information about the source locations. Besides the question whether unbiased estimates of...

By comparing the disadvantages and advantages of conventional algorithms of adaptive beamformer, we put forward an adaptive beamformer based on weight iterative algorithm for suppressing radio frequency interference (RFI), according to the distributing properties of RFI in different range cell. We get the initial weight vector of the adaptive beamformer by utilizing the algorithm of minimum var...

This paper develops a new approach to band subset selection (BSS) for hyperspectral image classification (HSIC) which selects multiple bands simultaneously as a band subset, referred to as simultaneous multiple band selection (SMMBS), rather than one band at a time sequentially, referred to as sequential multiple band selection (SQMBS), as most traditional band selection methods do. In doing so...

An adaptive beamformer that is robust to uncertainty in source direction-of-arrival (DOA) is derived using a Bayesian approach. The DOA is assumed to be a discrete random variable with a known a priori probability density function (pdf) that reflects the level of uncertainty in the source DOA. The resulting beamformer is a weighted sum of minimum variance distortionless response (MVDR) beamform...

In this paper we propose a distributed reformulation of the linearly constrained minimum variance (LCMV) beamformer for use in acoustic wireless sensor networks. The proposed distributed minimum variance (DMV) algorithm, for which we demonstrate implementations for both cyclic and acyclic networks, allows the optimal beamformer output to be computed at each node without the need for sharing raw...

Distant speech recognition over microphone arrays is challenging, especially in multi source environments. In this paper, a non reference anchor array (NRA) framework for distant speech recognition is proposed. The NRA framework uses a non reference anchor array to capture the interfering speech sources, in addition to the primary array that captures the speech source of interest. The framework...