Adaptive beamformer

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

Performance Analysis of Adaptive Array Signal Processing Algorithms

Abstract: Adaptive antenna is an array of antenna elements with signal processing capability to optimize its radiation pattern in response to the changing signal environment. The adaptive antenna improves the performance of wireless communication by increasing channel capacity and spectrum efficiency, and reduces the cost for establishing new wireless networks. Adaptive antenna aims at increasing the gain in the direction of desired user and direct nulls in the direction of interfering signals. It involves processing of signals induced on an array of antennas that can estimate the direction of radiating sources and calculate optimum weights for adaptive beamforming. This paper presents a performance evaluation of different direction of arrival (DOA) estimation and adaptive beamforming (ABF) algorithms. The simulation results show that multiple signal classification (MUSIC) algorithm provides more accurate and stable results among other DOA estimation techniques while recursive least square (RLS) algorithm s...

Automatic Generalized Loading for Robust Adaptive Beamforming

Abstract: The goal of this letter is to derive robust adaptive beamformers via generalized loading. In the proposed methods, Hermitian matrices are loaded on sample covariance matrix, and this is different from those methods based on the well-known diagonal loading approach. Furthermore, the computation of the loaded matrix is fully automatic, which is scarce in the literature. Numerical examples show that our methods are more robust to errors on array steering vector and sample covariance matrix than other tested parameter-free methods.

Adaptive beamforming in interference networks via bi-directional training

Abstract: We study distributed algorithms for adjusting beamforming vectors and receiver filters in multiple-input multiple-output (MIMO) interference networks, with the assumption that each user uses a single beam and a linear filter at the receiver. In such a setting there have been several distributed algorithms studied for maximizing the sum-rate or sum-utility assuming perfect channel state information (CSI) at the transmitters and receivers. The focus of this paper is to study adaptive algorithms for time-varying channels, without assuming any CSI at the transmitters or receivers. Specifically, we consider an adaptive version of the recent Max-SINR algorithm for a time-division duplex system. This algorithm uses a period of bi-directional training followed by a block of data transmission. Training in the forward direction is sent using the current beam-formers and used to adapt the receive filters. Training in the reverse direction is sent using the current receive filters as beams and used to adapt the transmit beamformers. The adaptation of both receive filters and beamformers is done using a least-squares objective for the current block. In order to improve the performance when the training data is limited, we also consider using exponentially weighted data from previous blocks. Numerical results are presented that compare the performance of the algorithms in different settings.

Null Steering of Adaptive Beamforming Using Linear Constraint Minimum Variance Assisted by Particle Swarm Optimization, Dynamic Mutated Artificial Immune System, and Gravitational Search Algorithm

Abstract: Linear constraint minimum variance (LCMV) is one of the adaptive beamforming techniques that is commonly applied to cancel interfering signals and steer or produce a strong beam to the desired signal through its computed weight vectors. However, weights computed by LCMV usually are not able to form the radiation beam towards the target user precisely and not good enough to reduce the interference by placing null at the interference sources. It is difficult to improve and optimize the LCMV beamforming technique through conventional empirical approach. To provide a solution to this problem, artificial intelligence (AI) technique is explored in order to enhance the LCMV beamforming ability. In this paper, particle swarm optimization (PSO), dynamic mutated artificial immune system (DM-AIS), and gravitational search algorithm (GSA) are incorporated into the existing LCMV technique in order to improve the weights of LCMV. The simulation result demonstrates that received signal to interference and noise ratio (SINR) of target user can be significantly improved by the integration of PSO, DM-AIS, and GSA in LCMV through the suppression of interference in undesired direction. Furthermore, the proposed GSA can be applied as a more effective technique in LCMV beamforming optimization as compared to the PSO technique. The algorithms were implemented using Matlab program.

System and method for automatic disabling and enabling of an acoustic beamformer

Abstract: A system and method that automatically disables and/or enables an acoustic beamformer is described herein The system and method automatically generates an output audio signal by applying beamforming to a plurality of audio signals produced by an array of microphones when it is determined that such beamforming is working effectively and generates the output audio signal based on an audio signal produced by a designated microphone within the array of microphones when it is determined that the beamforming is not working effectively Depending upon the implementation, the determination of whether the beamforming is working effectively may be based upon a measure of distortion associated with the beamformer response, an estimated level of reverberation, and/or the rate at which a computed look direction used to control the beamformer changes