We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
LMSK: a robust higher‐order gradient‐based adaptive algorithm.
- Authors
Kazemi Eghbal, Meysam; Alipoor, Ghasem
- Abstract
It has been shown that, in intensely noisy environments, adaptive algorithms based on higher‐order statistics can enjoy better performance, as compared with the well known second‐order least‐mean‐square (LMS) algorithm. By contrast, this advantage diminishes for low signal‐to‐noise ratio (SNR) levels, where the LMS algorithm outperforms. One remedy is to employ the LMS algorithm in conjunction with a higher‐order adaptation algorithm, in a mixed mode. Least‐mean kurtosis (LMK) is a higher‐order algorithm that has been shown to be advantageous to use if the noise distribution is Gaussian or super‐Gaussian. In this study, the authors propose the LMS/kurtosis algorithm, a stochastic gradient‐based adaptive algorithm that is a combination of the LMS and the LMK algorithms. Simulation results demonstrate the privilege of the proposed algorithm, in comparison with its counterparts, for a wide range of noise distributions and SNR levels. This improvement is achieved in spite of a negligible increase in computational complexity. An analytical model is also derived for the mean weight as well as the weight‐error covariance matrix, from which the mean‐square‐error behaviour of the algorithm can be predicted. Simulation results show the high accuracy of the derived model in different conditions.
- Publication
IET Signal Processing (Wiley-Blackwell), 2019, Vol 13, Issue 5, p506
- ISSN
1751-9675
- Publication type
Article
- DOI
10.1049/iet-spr.2018.5242