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.
Author(s): Yanwei Wang, Jian Li, Petre Stoica
Series: Synthesis Lectures on Signal Processing
Publisher: Morgan & Claypool Publishers
Year: 2005
Language: English
Pages: 108
KEYWORDS......Page 5
Contents......Page 6
Preface......Page 8
List of Abbreviations......Page 9
1.1 COMPLETE-DATA CASE......Page 10
1.2 MISSING-DATA CASE......Page 11
1.3 SUMMARY......Page 12
2.1 INTRODUCTION......Page 14
2.3 FORWARD-ONLY APES ESTIMATOR......Page 15
2.5 FORWARD–BACKWARD AVERAGING......Page 17
2.6 FAST IMPLEMENTATION......Page 20
3.1 INTRODUCTION......Page 22
3.2 GAPES......Page 23
3.3 TWO-DIMENSIONAL GAPES......Page 27
3.4 NUMERICAL EXAMPLES......Page 33
4.2 ML FITTING BASED SPECTRAL ESTIMATOR......Page 40
4.3 REMARKS ON THE ML FITTING CRITERION......Page 42
5.1 INTRODUCTION......Page 44
5.2 EM FOR MISSING-DATA SPECTRAL ESTIMATION......Page 45
5.3 MAPES-EM1......Page 46
5.4 MAPES-EM2......Page 50
5.5 ASPECTS OF INTEREST......Page 54
5.6 MAPES COMPARED WITH GAPES......Page 56
5.7 NUMERICAL EXAMPLES......Page 57
6.1 INTRODUCTION......Page 70
6.2 TWO-DIMENSIONAL ML-BASED APES......Page 71
6.3 TWO-DIMENSIONAL MAPES VIA EM......Page 73
6.4 TWO-DIMENSIONAL MAPES VIA CM......Page 81
6.5 MAPES-EM VERSUS MAPES-CM......Page 83
6.6 NUMERICAL EXAMPLES......Page 84
7.1 CONCLUDING REMARKS......Page 96
7.2 ONLINE SOFTWARE......Page 97
References......Page 100
JIAN LI......Page 106
PETRE STOICA......Page 107
