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Robust Sparse Normalized LMAT Algorithms for Adaptive System Identification Under Impulsive Noise Environments.
- Source :
-
Circuits, Systems & Signal Processing . Nov2019, Vol. 38 Issue 11, p5103-5134. 32p. - Publication Year :
- 2019
-
Abstract
- It is known that the conventional adaptive filtering algorithms can have good performance for non-sparse systems identification, but unsatisfactory performance for sparse systems identification. The normalized least mean absolute third (NLMAT) algorithm which is based on the high-order error power criterion has a strong anti-jamming capability against the impulsive noise, but reduced estimation performance in case of sparse systems. In this paper, several sparse NLMAT algorithms are proposed by inducing sparse-penalty functions into the standard NLMAT algorithm in order to exploit the system sparsity. Simulation results are given to validate that the proposed sparse algorithms can achieve a substantial performance improvement for a sparse system and robustness to impulsive noise environments. [ABSTRACT FROM AUTHOR]
- Subjects :
- *SYSTEM identification
*ADAPTIVE filters
*NOISE
*ALGORITHMS
Subjects
Details
- Language :
- English
- ISSN :
- 0278081X
- Volume :
- 38
- Issue :
- 11
- Database :
- Academic Search Index
- Journal :
- Circuits, Systems & Signal Processing
- Publication Type :
- Academic Journal
- Accession number :
- 139185780
- Full Text :
- https://doi.org/10.1007/s00034-019-01111-3