Paper Info
Title
Efficient Implementation of Iterative Adaptive Approach Spectral Estimation Techniques
Abstract
This paper presents computationally efficient implementations for several recent algorithms based on the iterative adaptive approach (IAA) for uniformly sampled one- and two-dimensional data sets, considering both the complete data case, and the cases when the data sets are missing samples, either lacking arbitrary locations, or having gaps or periodically reoccurring gaps. By exploiting the method's inherent low displacement rank, together with the development of suitable Gohberg-Semencul representations, and the use of data dependent trigonometric polynomials, the proposed implementations are shown to offer a reduction of the necessary computational complexity by at least one order of magnitude. Numerical simulations together with theoretical complexity measures illustrate the achieved performance gain.
Year
DOI
Venue
2011
10.1109/TSP.2011.2145376
IEEE Transactions on Signal Processing
Keywords
Field
DocType
efficient implementation,inherent low displacement rank,computationally efficient implementation,two-dimensional data set,iterative adaptive approach spectral,data dependent trigonometric polynomial,complete data case,theoretical complexity measure,estimation techniques,iterative adaptive approach,necessary computational complexity,arbitrary location,covariance matrix,numerical simulation,adaptive signal processing,estimation theory,iterative methods,polynomials,computational complexity,indexing terms,estimation,spectrum,convergence,spectral estimation,discrete fourier transform
Spectral density estimation,Mathematical optimization,Data set,Polynomial,Iterative method,Adaptive filter,Estimation theory,Covariance matrix,Mathematics,Computational complexity theory
Journal
Volume
Issue
ISSN
59
9
1053-587X
Citations 
PageRank 
References 
36
1.49
15
Authors
2
Name
Order
Citations
PageRank
G. O. Glentis1462.93
Andreas Jakobsson240943.32