Adaptive beamformer

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

Adaptive cardioid processing

Abstract: Algorithms for adaptively steering the null of a cardioid to achieve interference rejection are developed. The algorithms are basically broadband, but by frequency filtering it is easy to steer independently in different frequency bands. Of particular interest is the nulling of highly directional reverberation when sonobuoys are considered as multistatic active receivers. Simple adaptive algorithms are shown to be effective in simulations. >

HRTF-based robust least-squares frequency-invariant beamforming

Abstract: In this work, a Head-Related Transfer Function (HRTF)-based Robust Least-Squares Frequency-Invariant (RLSFI) beamformer design is proposed. The HRTF-based RLSFI beamformer accounts for the influence of a robot's head on the sound field. The performance of the new HRTF-based RLSFI beamformer is evaluated using signal-based measures and word error rates for an off-the-shelf speech recognizer, and is compared to the performance of the original free-field RLSFI beamformer design. The experimental results confirm the efficacy of the proposed HRTF-based beamformer design for robot audition.

Covariance Matrix Reconstruction via Residual Noise Elimination and Interference Powers Estimation for Robust Adaptive Beamforming

Abstract: Recently, a number of robust adaptive beamforming (RAB) methods based on Capon power spectrum estimator integrated over a specific region for covariance matrix reconstruction have been proposed. However, all of these methods ignore the residual noise existing in the Capon spectrum estimator, which results in reconstruction errors. In this paper, we propose a RAB algorithm via residual noise elimination and interference powers estimation to reconstruct covariance matrix. First, the proposed algorithm demonstrates the existence of residual noise and analyze its relationship to actual noise. Then, after eliminating the residual noise, the modified Capon power spectrum estimator is utilized to reconstruct the covariance matrix and desired signal SV. Moreover, to reduce the influence of the desired signal on interference powers estimation, we project the snapshots onto the complementary subspace of the desired signal and estimated interference powers are derived according to the theoretical formulation of the interference covariance matrix (ICM). The simulation results demonstrate that the proposed method is robust against various mismatches and can achieve superior performance.

Evaluation of reduced-rank, adaptive matched field processing algorithms for passive sonar detection in a shallow-water environment

Abstract: This paper evaluates the performance of several reduced-rank, adaptive matched field processing (AMFP) algorithms for passive sonar detection in a shallow-water environment. Effective rank reduction improves the stability of adaptive beamformer weight calculation when the number of available snapshots is limited. Here, rank-reduction techniques with various criteria for subspace selection are evaluated within a common framework and compared to the full-rank conventional and minimum-variance (MVDR) beamformers. Results from real data demonstrate that rank reduction, properly applied can improve AMFP detection performance in practical system implementations.

Adaptive array processor and processing method for communication system

Abstract: A processing circuit (50) in a communication system performs multi-channel adaptive array processing using a summed reference technique A plurality of adaptive modules (56 and 58) modify the magnitude and phase of antenna element signals from a plurality of antenna elements The outputs of the adaptive modules are combined by a combiner (60) to produce a summation signal A modem (64) processes and divides the summation signal into a plurality of reference channel signals on channels 1 through K A summer (70) adds the reference channel signals together to produce a composite reference signal that is subtracted by a subtractor (74) from the summation signal to produce an error signal The error signal is applied to the adaptive modules (56 and 58) which respond to nullify interfering and noise signals and to enhance the signal-to-interference ratio for the summation signal