Abstract
In this paper, shrinkage linear complex-valued least mean squares (SL-CLMS) and shrinkage widely linear complex-valued least mean squares (SWL-CLMS) algorithms are devised for adaptive beamforming. By exploiting the relationship between the noise-free a posteriori and a priori error signals, the SL-CLMS method is able to provide a variable step size to update the weight vector for the adaptive beamformer, significantly enhancing the convergence speed and decreasing the steady-state misadjustment. On the other hand, besides adopting a variable step size determined by minimizing the square of the augmented noise-free a posteriori errors, the SWL-CLMS approach exploits the noncircular properties of the signal of interest, which considerably improves the steady-state performance. Simulation results are presented to illustrate their superiority over the CLMS, complex-valued normalized LMS, variable step size, recursive least squares (RLS) algorithms and their corresponding widely linear-based schemes. Additionally, our proposed algorithms are more computationally efficient than the RLS solutions though they may have a slightly slower convergence rate.
Original language | English (US) |
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Article number | 6963464 |
Pages (from-to) | 119-131 |
Number of pages | 13 |
Journal | IEEE Transactions on Signal Processing |
Volume | 63 |
Issue number | 1 |
DOIs | |
State | Published - Jan 1 2015 |
Externally published | Yes |
Bibliographical note
Publisher Copyright:© 1991-2012 IEEE.
Keywords
- Complex-valued least mean squares (CLMS)
- convergence speed
- shrinkage
- steady-state
- variable step size
- widely linear