Denoising radiocommunications signals by using iterative wavelet shrinkage

• Published in: Applied statistics Vol. 51; no. 4; pp. 393 - 403
• Main Authors: Baxter, Paul D., Upton, Graham J. G.
• Format: Journal Article
• Language: English
• Published: Oxford, UK Blackwell Publishers 01-01-2002; Blackwell; Royal Statistical Society
• Series: Journal of the Royal Statistical Society Series C
• Subjects: Applied sciences; Artificial intelligence; Coefficients; Communication; Computer science; control theory; systems; Datasets; Denoising; Estimation methods; Exact sciences and technology; Fourier methods; Iterative methods; Linear inference, regression; Mathematics; Pattern recognition. Digital image processing. Computational geometry; Probability and statistics; Radio; Radiocommunications; Sciences and techniques of general use; Signal noise; Signals; Statistical analysis; Statistical methods; Statistical signal processing; Statistical variance; Statistics; Threshing; Wavelet analysis; Wavelet methods; White noise; Ridge regression; Fluctuations; Noise; Shrinkage estimator; Frequency domain method; Iterative method; Shrinkage; Statistical regression; Wavelets; Wavelet transformation; Broadcasting; Signal processing; Autocorrelation; Threshold
• ISSN: 0035-9254; 1467-9876
• DOI: 10.1111/1467-9876.00276

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.