Topic
Adaptive beamformer
About: Adaptive beamformer is a research topic. Over the lifetime, 4934 publications have been published within this topic receiving 93100 citations.
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TL;DR: It is shown that the convergence rate of eigenspace-based adaptive array beamforming depends on the number of array elements, the input signal-to-noise ratio (SNR), and the numberof interferers when the desired signal is present during the weight adaptation.
Abstract: In this paper, we analyze the statistical performance of several eigenspace-based adaptive array bearnformers, including conventional direct-form beamformers and generalized sidelobe canceler (GSC). It is shown that the convergence rate of eigenspace-based adaptive array beamforming depends on the number of array elements, the input signal-to-noise ratio (SNR), and the number of interferers when the desired signal is present during the weight adaptation. In contrast, the misadjostment of the array output power is a function of the number of interferers and is independent of the number of array elements when the desired signal is absent. Several simulation examples are also provided to confirm the theoretical results.
25 citations
07 May 1996
TL;DR: An array of microphones which achieves good performance in double talk situations by jointly identifying the acoustic paths from the speech and echo sources and with an adaptive beamformer constrained over these paths properly recover the original speech, cancel the echo and reduce the background noise.
Abstract: We propose in this paper an array of microphones which achieves good performance in double talk situations. By a subspace tracking procedure, we jointly identify the acoustic paths from the speech and echo sources. With an adaptive beamformer constrained over these paths, we properly recover the original speech, cancel the echo and reduce the background noise. Simulations made with real data confirm the efficiency of this array.
25 citations
TL;DR: In this article, a robust adaptive beamforming technique based on a reconstructed covariance matrix and array steering vector (ASV) estimation using low-complexity algorithms is proposed, which has better performance yet does not involve any specific optimisation program even if arbitrary ASV imperfections are considered.
Abstract: A robust adaptive beamforming technique based on a reconstructed covariance matrix and array steering vector (ASV) estimation using low-complexity algorithms is proposed. The spatial spectrum estimator is used to reconstruct the interference-plus-noise covariance matrix and the ASV estimation process is used to calculate the correlation coefficients of the assumed ASV and the eigenvectors. Contrary to other robust adaptive beamforming methods, this approach has better performance yet does not involve any specific optimisation programme even if arbitrary ASV imperfections are considered. Simple implementation and significant signal-to-interference-plus-noise ratio enhancement support the practicability of the proposed method.
25 citations
TL;DR: The proposed reduced-complexity algorithm is very effective in (even severe) multipath conditions, outperforming natural competitors also when the number of antennas and samples is kept at the theoretical minimum, and exhibiting robustness to several types of mismatch.
25 citations
04 Aug 2002
TL;DR: The proposed EBAE eigen vector/beam association and excision (EBAE) algorithm, associates each eigenvector with a beam or angle and removes it from that beam only and is shown to provide improved performance both in terms of interference rejection and signal separation.
Abstract: A new eigenvector-based robust adaptive beamforming (ABF) algorithm is presented for use with passive sonar arrays. Robustness refers to protection against self-nulling in the presence of signal mismatch. The algorithm is based on an eigenvector decomposition, similar to ABF algorithms such as dominant mode rejection (DMR) and principal components inverse (PCI). For robustness, the proposed method removes the eigenvector from the ABF weight computation deemed as the contributor to self-nulling. This approach is in contrast to eigenvector-based ABF with a white noise gain constraint which instead changes the weighting of all eigenvectors. By identifying and removing the offending eigenvector, robustness to self-nulling can be restored. The proposed eigenvector/beam association and excision (EBAE) algorithm, associates each eigenvector with a beam or angle and removes it from that beam only. The EBAE eigenvector-based ABF is compared to other robust ABF algorithms in both simulation and experimental data where it is shown to provide improved performance both in terms of interference rejection and signal separation.
25 citations