Spectral density estimation

Spectroscopy

Algorithm

Statistics

Spectral analysis

Telecommunications

Data mining

Computer science

Filter (signal processing)

Sonar

Missing data

Physics

Mathematics

Astronomy

Geography

Synthetic aperture radar

Nonparametric statistics

Environmental science

Mathematical analysis

Artificial intelligence

Remote sensing

Radar

Fourier transform

Machine learning

Computer vision

Synthesis Lectures on Signal Processing

Spectral estimation is important in many fields including astronomy, meteorology, seismology, communications, economics, speech analysis, medical imaging, radar, sonar, and underwater acoustics. Most existing spectral estimation algorithms are devised for uniformly sampled complete-data sequences. However, the spectral estimation for data sequences with missing samples is also important in many applications ranging from astronomical time series analysis to synthetic aperture radar imaging with angular diversity. For spectral estimation in the missing-data case, the challenge is how to extend the existing spectral estimation techniques to deal with these missing-data samples. Recently, nonparametric adaptive filtering based techniques have been developed successfully for various missing-data problems. Collectively, these algorithms provide a comprehensive toolset for the missing-data problem based exclusively on the nonparametric adaptive filter-bank approaches, which are robust and accurate, and can provide high resolution and low sidelobes. In this book, we present these algorithms for both one-dimensional and two-dimensional spectral estimation problems.

Spectral Analysis of Signals: The Missing Data Case