Adaptive beamformer

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

Adaptive beamforming using variable step-size LMS algorithm with novel ULA array configuration

Abstract: This paper proposes a new array geometry and a variable step-size (VSS) LMS algorithm to improve adaptive beamforming in smart antenna systems. The beamformer based on the proposed array geometry coupled with the use of VSSLMS is capable of resolving signals arriving from narrowband sources propagating plane waves close to the array endfire. The proposed array configuration comprises of doubly crossed uniform linear arrays (ULAs) so that the size and computational load of the proposed array is identical to that of a 2N-element conventional ULA, while beamforming accuracy and angular resolution are higher for narrowband signals arriving at directions close to the array endfire. Performance of the VSSLMS algorithm and proposed ULA configuration is investigated with respect to the variation of a number of parameters related to the signal environment and array itself. Results of numerical simulation are used to design smart antenna systems with optimal performance.

Near optimal robust adaptive beamforming approach based on evolutionary algorithm

Abstract: The presence of desired signal in the training data for sample covariance matrix calculation is known to lead to a substantial performance degradation, especially when the desired signal is the dominant signal in the training data. Together with the uncertainty in the look direction, most of the adaptive beamforming solutions are unable to approach the optimal performance. In this paper, we propose an evolutionary algorithm (EA) based robust adaptive beamforming that is able to achieve near optimal performance. The essence of the idea is to shape the array beam response such that it has maximum response in the desired signal’s angular range and minimum response in the interferences’ angular range. In addition, the approach introduces null-response constraints deduced from the array observation to achieve better interference cancelation performance. As a whole, the proposed optimization is solvable using an improved variant of the differential evolution (DE) algorithm. Numerical simulations are also presented to demonstrate the efficacy of the proposed algorithm.

On Diagonal Loading for Robust Adaptive Beamforming Based on Worst-Case Performance Optimization

Abstract: Robust adaptive beamforming based on worst-case performance optimization is investigated in this paper. It improves robustness against steering vector mismatches by the approach of diagonal loading. A closed-form solution to optimal loading is derived after some approximations. Besides reducing the computational complexity, it shows how different factors affect the optimal loading. Based on this solution, a performance analysis of the beamformer is carried out. As a consequence, approximated closed-form expressions of the source-of-interest power estimation and the output signalto-interference-plus-noise ratio are presented in order to predict its performance. Numerical examples show that the proposed closed-form expressions are very close to their actual values.

Robust Adaptive Beamforming with Sensor Position Errors Using Weighted Subspace Fitting-Based Covariance Matrix Reconstruction.

Abstract: When sensor position errors exist, the performance of recently proposed interference-plus-noise covariance matrix (INCM)-based adaptive beamformers may be severely degraded. In this paper, we propose a weighted subspace fitting-based INCM reconstruction algorithm to overcome sensor displacement for linear arrays. By estimating the rough signal directions, we construct a novel possible mismatched steering vector (SV) set. We analyze the proximity of the signal subspace from the sample covariance matrix (SCM) and the space spanned by the possible mismatched SV set. After solving an iterative optimization problem, we reconstruct the INCM using the estimated sensor position errors. Then we estimate the SV of the desired signal by solving an optimization problem with the reconstructed INCM. The main advantage of the proposed algorithm is its robustness against SV mismatches dominated by unknown sensor position errors. Numerical examples show that even if the position errors are up to half of the assumed sensor spacing, the output signal-to-interference-plus-noise ratio is only reduced by 4 dB. Beam patterns plotted using experiment data show that the interference suppression capability of the proposed beamformer outperforms other tested beamformers.

Multiple-input multiple-output multicarrier communication system with joint transmitter and receiver adaptive beamforming for enhanced signal-to-noise ratio

Abstract: A system for communicating through a multicarrier communication channel estimates a channel transfer function H nm (K) each of a plurality of subcarriers of a multicarrier communication channel from received training signals. The system also estimates a spatial correlation matrix R nm (k) for each subcarrier from noise and interference samples obtained during the reception of the training signals through two or more receive-signal paths. Receiver and transmitter beamformer weights may be generated for the subcarriers using the channel transfer function H nm (k) and the spatial correlation matrices R nm (k) for use in subsequent communication through the channel.