Matrix computations (3rd ed.)
A network for recursive extraction of canonical coordinates
Neural Networks - 2003 Special issue: Advances in neural networks research IJCNN'03
Adaptive MC-CDMA receiver with constrained constant modulus IQRD-RLS algorithm for MAI suppression
Signal Processing - Special section: Security of data hiding technologies
IEEE Transactions on Signal Processing
IEEE Transactions on Communications
A fast least-squares algorithm for linearly constrained adaptivefiltering
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Subspace Expansion and the Equivalence of Conjugate Direction and Multistage Wiener Filters
IEEE Transactions on Signal Processing - Part II
Reduced-rank adaptive filtering
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Recursive least squares constant modulus algorithm for blind adaptive array
IEEE Transactions on Signal Processing
An iterative algorithm for the computation of the MVDR filter
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing - Part I
Blind multiuser detection: a subspace approach
IEEE Transactions on Information Theory
A multistage representation of the Wiener filter based on orthogonal projections
IEEE Transactions on Information Theory
On the equivalence of three reduced rank linear estimators with applications to DS-CDMA
IEEE Transactions on Information Theory
Hi-index | 35.68 |
This paper proposes a robust reduced-rank scheme for adaptive beamforming based on joint iterative optimization (JIO) of adaptive filters. The novel scheme is designed according to the constant modulus (CM) criterion subject to different constraints. The proposed scheme consists of a bank of full-rank adaptive filters that forms the transformation matrix,and an adaptive reduced-rank filter that operates at the output of the bank of filters to estimate the desired signal. We describe the proposed scheme for both the direct-form processor (DFP) and the generalized sidelobe canceller (GSC) structures. for each structure, we derive stochastic gradient (SG) and recursive least squares (RLS) algorithms for its adaptive implementation.The Gram-Schmidt (GS) technique is applied to the adaptive algorithms for reformulating the transformation matrix and improving the performance. An automatic rank selection technique is developed and employed to determine the most adequate rank for the derived algorithms. A detailed complexity study and a convexity analysis are carried out. Simulation results show that the proposed algorithms outperform the existing full-rank and reduced-rank methods in convergence and tracking performance.