A recursive least squares implementation for LCMP beamforming underquadratic constraint

Authors:
Zhi Tian;K.L. Bell;H.L. Van Trees
Affiliations:
Dept. of Electr. & Comput. Eng., Michigan Technol. Univ., Houghton, MI;-;-
Venue:
IEEE Transactions on Signal Processing
Year:
2001

Citing 0
Cited 11

Spatially pre-processed speech distortion weighted multi-channel Wiener filtering for noise reduction

Signal Processing
A neural network for robust LCMP beamforming

Signal Processing - Fractional calculus applications in signals and systems
Recursive and fast recursive capon spectral estimators

EURASIP Journal on Applied Signal Processing
Robust multiuser detection based on variable loading RLS technique

Signal Processing
A compact cooperative recurrent neural network for computing general constrained L1norm estimators

IEEE Transactions on Signal Processing
Adaptive linearly constrained minimum variance beamforming for multiuser cooperative relaying using the kalman filter

IEEE Transactions on Wireless Communications
Robust Capon beamformer under norm constraint

Signal Processing
Adaptive feed-forward and feedback control using neural networks for oxygen ratio in fuel cell stacks

Proceedings of the 12th International Conference on Computer Systems and Technologies
Robust Capon beamforming against large DOA mismatch

Signal Processing
Robust adaptive beamforming method using principal eigenpairs with modification of PASTd

Digital Signal Processing
Non-intrusive Cognitive Radio Using Adaptive Nulls-Steering

Wireless Personal Communications: An International Journal

Quantified Score

Hi-index	35.69

Visualization

Abstract

Quadratic constraints on the weight vector of an adaptive linearly constrained minimum power (LCMP) beamformer can improve robustness to pointing errors and to random perturbations in sensor parameters. We propose a technique for implementing a quadratic inequality constraint with recursive least squares (RLS) updating. A variable diagonal loading term is added at each step, where the amount of loading has a closed-form solution. Simulations under different scenarios demonstrate that this algorithm has better interference suppression than both the RLS beamformer with no quadratic constraint and the RLS beamformer using the scaled projection technique, as well as faster convergence than LMS beamformers