Distributed predictive subspace pursuit

Author

Sundman, Dennis ; Zachariah, Dave ; Chatterjee, Saptarshi ; Skoglund, Mikael

Author_Institution

Sch. of Electr. Eng., KTH - R. Inst. of Technol., Stockholm, Sweden

fYear

2013

Firstpage

4633

Lastpage

4637

Abstract

In a compressed sensing setup with jointly sparse, correlated data, we develop a distributed greedy algorithm called distributed predictive subspace pursuit. Based on estimates from neighboring sensor nodes, this algorithm operates iteratively in two steps: first forming a prediction of the signal and then solving the compressed sensing problem with an iterative linear minimum mean squared estimator. Through simulations we show that the algorithm provides better performance than current state-of-the-art algorithms.

Keywords

compressed sensing; distributed algorithms; greedy algorithms; iterative methods; least mean squares methods; prediction theory; compressed sensing problem; correlated data; distributed greedy algorithm; distributed predictive subspace pursuit; iterative linear minimum mean squared estimator; jointly sparse data; sensor nodes; signal prediction; Compressed sensing; Conferences; Correlation; Prediction algorithms; Sensors; Sparse matrices; Vectors; compressed sensing; distributed compressed sensing; greedy algorithms; prediction methods;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech and Signal Processing (ICASSP), 2013 IEEE International Conference on

Conference_Location

Vancouver, BC

ISSN

1520-6149

Type

conf

DOI

10.1109/ICASSP.2013.6638538

Filename

6638538