EFFICIENT SPECTRAL ANALYSIS IN THE MISSING DATA CASE USING SPARSE ML METHODS

被引：0

作者：

Glentis, George-Othon ^{[1
]}

Karlsson, Johan ^{[2
]}

Jakobsson, Andreas ^{[3
]}

Li, Jian ^{[4
]}

机构：

[1] Univ Peloponnese, Dept Informat & Telecommun, Tripoli, Greece

[2] KTH Royal Inst Technol, Dept Math, Stockholm, Sweden

[3] Lund Univ, Dept Math Stat, S-22100 Lund, Sweden

[4] Univ Florida, Dept Elect & Comp Engn, Gainesville, FL 32611 USA

来源：

2014 PROCEEDINGS OF THE 22ND EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO) | 2014年

基金：

瑞典研究理事会; 美国国家科学基金会;

关键词：

Spectral estimation theory and methods; Sparse Maximum Likelihood methods; fast algorithms; ITERATIVE ADAPTIVE APPROACH; TOEPLITZ MATRICES; DISPLACEMENT;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Given their wide applicability, several sparse high-resolution spectral estimation techniques and their implementation have been examined in the recent literature. In this work, we further the topic by examining a computationally efficient implementation of the recent SMLA algorithms in the missing data case. The work is an extension of our implementation for the uniformly sampled case, and offers a notable computational gain as compared to the alternative implementations in the missing data case.

引用

页码：1746 / 1750

页数：5

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