Adaptive beamformer

About: Adaptive beamformer is a research topic. Over the lifetime, 4934 publications have been published within this topic receiving 93100 citations.

An architecture for an embedded antenna-array digital GNSS receiver using subspace-based methods for spatial filtering

Abstract: This paper presents an architecture for an embedded multi-antenna digital GNSS receiver. A two-stage adaptive beamformer for interference suppression and Line-of-Sight (LoS) signal amplification is presented and analyzed w.r.t. to an efficient implementation on embedded receivers. Jammer signals are mitigated at pre-correlation stage whereas the LoS signals are amplified at post-correlation stage. The method is based on a subspace-based approach where filter coefficients are derived from the eigenvalues and -vectors of the covariance matrix. In the first stage, the covariance matrix is determined immediately from the digital antenna signals for interference mitigation and in the second stage, the matrix is computed based on the correlator outputs of each satellite in LoS. Dedicated buildingblocks for covariance matrix estimation and filtering are required for interference mitigation since this operation is computed on sampling rate. A fixed-point VHDL implementation and related costs in terms of logic-cell requirements on an FPGA are provided for both blocks. Eigendecomposition is computed on an embedded processor. The implementation of two decomposition algorithms (one for interference mitigation and the other one for LoS-signal amplification) are presented. Optimizations and costs in terms of processing-cycles on an embedded processor are provided.

A beamformer post-filter for cochlear implant noise reduction.

Abstract: Cochlear implant users have limited ability to understand speech in noisy conditions. Signal processing methods to address this issue that use multiple microphones typically use beamforming to perform noise reduction. However, the effectiveness of the beamformer is diminished as the number of interfering noises increases and the acoustic environment becomes more diffuse. A multi-microphone noise reduction algorithm that aims to address this issue is presented in this study. The algorithm uses spatial filtering to estimate the signal-to-noise ratio (SNR) and attenuates time-frequency elements that have poor SNR. The algorithm was evaluated by measuring intelligibility of speech embedded in 4-talker babble where the interfering talkers were spatially separated and changed location during the test. Twelve cochlear implant users took part in the evaluation, which demonstrated a significant mean improvement of 4.6 dB (standard error 0.4, P < 0.001) in speech reception threshold compared to an adaptive beamformer. The results suggest that a substantial improvement in performance can be gained for cochlear implant users in noisy environments where the noise is spatially separated from the target speech.

General-rank transmit beamforming for multi-group multicasting networks using OSTBC

Abstract: This paper addresses adaptive beamforming in multi-group multicasting networks where groups of users subscribe to independent services that are simultaneously served by the base station. Beamformers are designed to maximize the minimum signal-to-interference-plus-noise ratio (SINR) of the users in all groups subject to a total transmit power constraint. By combining multi-group multicast beamforming with Alamouti space-time block coding, the degrees of freedom in the beamformer design is doubled resulting in drastically improved beamforming performance. In our paper we extend recent approaches in [1] and [2] for rank-two beamforming, originally devised for single-group multicasting networks that are free of multi-user interference, to multi-group multicasting networks, where multi-user interference represents a major challenge. Simulation results demonstrate that the proposed approach significantly outperforms the existing approaches.

Dual-Domain Adaptive Beamformer Under Linearly and Quadratically Constrained Minimum Variance

Abstract: In this paper, a novel adaptive beamforming algorithm is proposed under a linearly and quadratically constrained minimum variance (LQCMV) beamforming framework, based on a dual-domain projection approach that can efficiently implement a quadratic-inequality constraint with a possibly rank-deficient positive semi-definite matrix, and the properties of the proposed algorithm are analyzed. As an application, relaxed zero-forcing (RZF) beamforming is presented which adopts a specific quadratic constraint that bounds the power of residual interference in the beamformer output with the aid of interference-channel side-information available typically in wireless multiple-access systems. The dual-domain projection in this case plays a role in guiding the adaptive algorithm towards a better direction to minimize the interference and noise, leading to considerably faster convergence. The robustness issue against channel mismatch and ill-posedness is also addressed. Numerical examples show that the efficient use of interference side-information brings considerable gains.