Premium
Denoising radiocommunications signals by using iterative wavelet shrinkage
Author(s) -
Baxter Paul D.,
Upton Graham J. G.
Publication year - 2002
Publication title -
journal of the royal statistical society: series c (applied statistics)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.205
H-Index - 72
eISSN - 1467-9876
pISSN - 0035-9254
DOI - 10.1111/1467-9876.00276
Subject(s) - wavelet , computer science , noise reduction , second generation wavelet transform , noise (video) , wavelet transform , context (archaeology) , speech recognition , pattern recognition (psychology) , term (time) , contrast (vision) , algorithm , wavelet packet decomposition , discrete wavelet transform , signal processing , signal (programming language) , stationary wavelet transform , artificial intelligence , mathematics , digital signal processing , physics , paleontology , quantum mechanics , computer hardware , image (mathematics) , biology , programming language
Summary. Radiocommunications signals pose particular problems in the context of statistical signal processing. This is because short‐term fluctuations (noise) are a consequence of atmospheric effects whose characteristics vary in both the short and the longer term. We contrast traditional time domain and frequency domain filters with wavelet methods. We also propose an iterative wavelet procedure which appears to provide benefits over existing wavelet methods.