A Fast Converging Biased NLMS Algorithm
YanLing Hao, Ying Cui, YongGang Zhang
Available Online December 2013.
- https://doi.org/10.2991/icaiees-13.2013.14How to use a DOI?
- biased NLMS algorithm, low signal-to-noise ratio, system identification
- It’s well known, biased estimation can effectively reduce the steady-state mean-square error (MSE), as for system identification with a low signal-to-noise ratio (SNR).A new biased normalized least-mean-squares (NLMS) algorithm is proposed in this paper. In the proposed algorithm the optimal solution is deduced on the basis of maximizing the decrease of the mean square deviation (MSD). To facilitate practical application approximation and parameter choice guidelines are provided for the new algorithm. The proposed algorithm is confirmed by simulations to obtain both a small steady-state excess MSE (EMSE) and a fast convergence rate, and to outperform the existing convex combination of two NLMS filters (CNLMS) algorithm. Steady state performance and transient performance analyses are provided to interpret the simulation results.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - YanLing Hao AU - Ying Cui AU - YongGang Zhang PY - 2013/12 DA - 2013/12 TI - A Fast Converging Biased NLMS Algorithm BT - Proceedings of the 2013 International Conference on Advanced Information Engineering and Education Science (ICAIEES 2013) PB - Atlantis Press SP - 47 EP - 52 SN - 1951-6851 UR - https://doi.org/10.2991/icaiees-13.2013.14 DO - https://doi.org/10.2991/icaiees-13.2013.14 ID - Hao2013/12 ER -