Adaptive Reduced-Rank Constrained Constant Modulus Algorithms Based on Joint Iterative Optimization of Filters for Beamforming

• Published in: IEEE transactions on signal processing Vol. 58; no. 6; pp. 2983 - 2997
• Main Authors: Lei Wang, de Lamare, Rodrigo C, Yukawa, Masahiro
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.06.2010; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive algorithm; Adaptive algorithms; Adaptive filters; Algorithms; Antenna array; Applied sciences; Array signal processing; Banks; Beamforming; constrained constant modulus; Constraint optimization; Detection, estimation, filtering, equalization, prediction; Exact sciences and technology; Filter bank; Information, signal and communications theory; Iterative algorithms; Least squares methods; Miscellaneous; Optimization; reduced-rank; Resonance light scattering; Robustness; Sidelobes; Signal and communications theory; Signal processing; Signal, noise; Stochastic processes; Studies; Telecommunications and information theory; Transformations; Performance evaluation; Adaptive algorithm; Noise reduction; Automatic selection; Iterative method; Gram Schmidt method; Stochastic method; Beam forming; Implementation; Optimization; reduced-rank; Least squares method; Filter bank; Convexity; Antenna array; Numerical convergence; Target tracking; Adaptive filter; Joint; Recursive algorithm; Constant modulus algorithm; Interference suppression; Simulation; Recursive method; Signal processing; beamforming; constrained constant modulus
• ISSN: 1053-587X; 1941-0476
• DOI: 10.1109/TSP.2010.2044250

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.