A neural-network-based linearly constrained minimum variance beamformer

Evaluation of a Neural-Network-Based Adaptive Beamforming Scheme With Magnitude-Only Constraints

“…Among the various beamforming schemes (and corresponding optimality criteria) available in the literature [2], here we focus on the class of algorithms based on Capon's method [9], which was generalized in [16,17] to the case of multiple desired signals. Such algorithms are based on the minimization of the mean output power,…”

“…This has motivated a growing interest toward implementations based on artificial neural networks (ANNs) [12], which are capable, via suitable training procedures, of accurately approximating complex nonlinear mappings, and admit very-largescale-integration hardware implementations. ANN-based algorithms have been successfully applied to several electromagnetics engineering optimization/synthesis problems (see, e.g., [13][14][15]) and, in particular, to adaptive beamforming schemes based on linearly-constrained MSE minimization [16,17].…”

“…In this paper, we propose an alternative scheme based on the MSE minimization which, unlike those in [16,17], involves magnitude-only (nonlinear) constraints on the array-factor in the desired-signal directions. As compared with the scheme in [16,17], for each constraint, we recover a real degree of freedom (arrayfactor phase) which we exploit in the optimization process for performance improvement.…”

“…As compared with the scheme in [16,17], for each constraint, we recover a real degree of freedom (arrayfactor phase) which we exploit in the optimization process for performance improvement. Via the Lagrange theory of constrained optimization [18], we reduce the problem to the unconstrained optimization of a non-quadratic functional (depending on the above auxiliary phase unknowns), for which we propose an iterative solution scheme.…”

“…Via the Lagrange theory of constrained optimization [18], we reduce the problem to the unconstrained optimization of a non-quadratic functional (depending on the above auxiliary phase unknowns), for which we propose an iterative solution scheme. Moreover, we address the ANN implementation, and assess the overall performance by comparison with standard schemes [16,17].…”