Name
Abstract | ||
|---|---|---|
How to choose a wavelet basis for a given signal is always important and difficult in wavelet applications. In this paper, based on the fact that the Morlet has been conventionally selected to make wavelet analysis of LFM signals, some further research on the wavelet basis selection are done. Morlet is in fact not the best choice under all application conditions related to LFM signals processing, which is proved by both the theoretical analysis and the simulation results in this paper. So we should not choose a wavelet basis randomly, but weigh it synthetically. © 2006 IEEE. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/ICCIAS.2006.295381 | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
Keywords | Field | DocType |
null | Lifting scheme,Gabor wavelet,Computer science,Artificial intelligence,Discrete wavelet transform,Wavelet packet decomposition,Wavelet transform,Wavelet,Pattern recognition,Speech recognition,Second-generation wavelet transform,Stationary wavelet transform,Machine learning | Conference |
Volume | Issue | ISSN |
2 | null | null |
ISBN | Citations | PageRank |
1-4244-0605-6 | 1 | 0.56 |
References | Authors | |
5 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cui Hua | 1 | 3 | 1.27 |
| Song Guoxiang | 2 | 1 | 0.56 |