Name
Abstract | ||
|---|---|---|
In this paper, we develop a novel constrained adaptive filtering algorithm called constrained recursive least p-power (CRLP) algorithm, which incorporates a set of linear constraints into the least mean p-power error (LMP) criterion to solve a constrained optimization problem directly. Compared with the conventional constrained adaptive filtering algorithms including constrained least mean square (CLMS), constrained recursive least square (CRLS) and constrained least mean p-power (CLMP), CRLP can achieve better performance under non- Gaussian noises. Simulation results are presented to confirm the superior performance of the new algorithm. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ICDSP.2018.8631663 | DSL |
Keywords | Field | DocType |
Signal processing algorithms,Convergence,Filtering,Simulation,Adaptive filters,Filtering algorithms,Gaussian noise | Convergence (routing),Least squares,Least mean squares filter,Computer science,Filter (signal processing),Algorithm,Gaussian,Adaptive filter,Gaussian noise,Recursion | Conference |
ISSN | ISBN | Citations |
1546-1874 | 978-1-5386-6811-5 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiajun Sun | 1 | 1 | 2.06 |
| Siyuan Peng | 2 | 22 | 3.41 |
| Qinglai Liu | 3 | 0 | 0.68 |
| Ruijie Zhao | 4 | 34 | 7.26 |
| Zhiping Lin | 5 | 291 | 37.46 |