A recursive least squares implementation for LCMP beamforming under quadratic constraint
Document Type
Article
Publication Date
6-1-2001
Abstract
Quadratic constraints on the weight vector of an adaptive linearly constrained minimum power (LCMP) beam-former can improve robustness to pointing errors and to random perturbations in sensor parameters. In this paper, 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.
Publication Title
IEEE Transactions on Signal Processing
Recommended Citation
Tian, Z.,
Bell, K.,
&
Van Trees, H.
(2001).
A recursive least squares implementation for LCMP beamforming under quadratic constraint.
IEEE Transactions on Signal Processing,
49(6), 1138-1145.
http://doi.org/10.1109/78.923296
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/10257