Reduced-rank Adaptive Constrained Constant Modulus Beamforming Algorithms based on Joint Iterative Optimization of Filters

TL;DR

This paper introduces two novel reduced-rank adaptive beamforming algorithms based on joint iterative optimization under the constant modulus criterion, demonstrating superior performance through simulations.

Contribution

It develops two new reduced-rank algorithms for adaptive beamforming using joint iterative optimization and orthogonal constraints, enhancing performance over existing methods.

Findings

Algorithms outperform existing methods in simulations

Orthogonal constraint via Gram-Schmidt improves robustness

Reduced-rank approach lowers computational complexity

Abstract

This paper proposes a reduced-rank scheme for adaptive beamforming based on the constrained joint iterative optimization of filters. We employ this scheme to devise two novel reduced-rank adaptive algorithms according to the constant modulus (CM) criterion with different constraints. The first devised algorithm is formulated as a constrained joint iterative optimization of a projection matrix and a reduced-rank filter with respect to the CM criterion subject to a constraint on the array response. The constrained constant modulus (CCM) expressions for the projection matrix and the reduced-rank weight vector are derived, and a low-complexity adaptive algorithm is presented to jointly estimate them for implementation. The second proposed algorithm is extended from the first one and implemented according to the CM criterion subject to a constraint on the array response and an orthogonal constraint on the projection matrix. The Gram-Schmidt (GS) technique is employed to achieve this orthogonal constraint and improve the performance. Simulation results are given to show superior performance of the proposed algorithms in comparison with existing methods.